Connectivity & Convergence for Smart Living Publication
| Auhtor | Program | Title | Abstract | Details |
|---|---|---|---|---|
| ACHMAD RIZAL | Connectivity & Convergence Instruments for Smart Living | Comparison of Multiscale Entropy Techniques for Lung Sound Classification | Lung sound is a biological signal that can be used to determine the health level of the respiratory tract. Various digital signal processing techniques have been developed for automatic classification of lung sounds. Entropy is one of the parameters used to measure the biomedical signal complexity. Multiscale entropy is introduced to measure the entropy of a signal at a particular scale range. Over time, various multiscale entropy techniques have been proposed to measure the complexity of biological signals and other physical signals. In this paper, some multiscale entropy techniques for lung sound classification are compared. The result of the comparison indicates that the Multiscale Permutation Entropy (MPE) produces the highest accuracy of 97.98% for five lung sound datasets. The result was achieved for the scale 1-10 producing ten features for each lung sound data. This result is better than other seven entropies. Multiscale entropy analysis can improve the accuracy of lung sound classification without requiring any features other than entropy. | Paper Link |
| ACHMAD RIZAL | Connectivity & Convergence Instruments for Smart Living | Fractal Dimension for Lung Sound Classification in Multiscale Scheme | Lung sound is a biological signal with the information of respiratory system health. Health lung sound can be differentiated from other pathological sounds by auscultation. This difference can be objectively analyzed by a number of digital signal processing techniques. One method in analyzing the lung sound is signal complexity analysis using fractal dimension. To improve the accuracy of lung sound classification, Fractal Dimension (FD) is calculated in the multiscale signal using the coarse-grained procedure. The combination of FD and multiscale process generates the more comprehensive information of lung sound. This study used seven types of FD and three types of the classifier. The result showed that Petrosian C in signal with the scale of 1-5 and SVM with fine Gaussian kernel had the highest accuracy of 99% for five classes of lung sound data. The proposed method can be used as an alternative method for computerized lung sound analysis to assist the doctors in the early diagnosis of lung disease. | Paper Link |
| ACHMAD RIZAL | Connectivity & Convergence Instruments for Smart Living | Comparison of Multi-distance Signal Level Difference Hjorth Descriptor and its Variations for Lung Sound Classification | A biological signal has the multi-scale and signals complexity properties. Many studies have used the signal complexity calculation methods and multi-scale analysis to analyze the biological signal, such as lung sound. Signal complexity methods used in the biological signal analysis include entropy, fractal analysis, and Hjorth descriptor. Meanwhile, the commonly used multi-scale methods include wavelet analysis, coarse-grained procedure, and empirical mode decomposition (EMD). One of the multi-scale methods in the biological signal analysis is the multi-distance signal level difference (MSLD), which calculates a difference between two signal samples at a specific distance. In previous studies, MSLD was combined with Hjorth descriptor for lung sound classification. MSLD has the potential to be developed by modifying the fundamental equation of MSLD. This study presents the comparison of MSLD and its variations combined with Hjorth descriptor for lung sound classification. The results showed that MSLD and its variations had the highest accuracy of 98.99% for five lung sound data classes. The results of this study provided several alternatives for multi-scale signal complexity analysis method for biological signals. | Paper Link |
| ACHMAD RIZAL | Connectivity & Convergence Instruments for Smart Living | Lung Sound Classification using Hjorth Descriptor Measurement on Wavelet Sub-bands | Signal complexity is one point of view to analyze the biological signal. It arises as a result of the physiological signal produced by biological systems. Signal complexity can be used as a method in extracting the feature for a biological signal to differentiate a pathological signal from a normal signal. In this research, Hjorth descriptors, one of the signal complexity measurement techniques, were measured on signal sub-band as the features for lung sounds classification. Lung sound signal was decomposed using two wavelet analyses: discrete wavelet transform (DWT) and wavelet packet decomposition (WPD). Meanwhile, multi-layer perceptron and N-fold cross-validation were used in the classification stage. Using DWT, the highest accuracy was obtained at 97.98%, while using WPD, the highest one was found at 98.99%. This result was found better than the multiscale Hjorth descriptor as in previous studies. | Paper Link |
| ACHMAD RIZAL | Connectivity & Convergence Instruments for Smart Living | Comparison of Multilevel Wavelet Packet Entropy Using Various Entropy Measurement for Lung Sound Classification | Wavelet Entropy (WE) is one of the entropy measurement methods by means of the discrete wavelet transform (DWT) subband. Some of the developments of WE are wavelet packet entropy (WPE), wavelet time entropy. WPE has several variations such as the Shannon entropy calculation on each subband of WPD that produces 2N entropy or WPE, which yields an entropy value. One of the WPE improvements is multilevel wavelet packet entropy (MWPE), which yields entropy value as much as N decomposition level. In a previous research, MWPE was calculated using Shannon method; hence, in this research MWPE calculation was done using Renyi and Tsallis method. The results showed that MWPE using Shannon calculation could yield the highest accuracy of 97.98% for N = 4 decomposition level. On the other hand, MWPE using Renyi entropy yielded the highest accuracy of 93.94% and the one using Tsallis entropy yielded 57.58% accuracy. Here, the test was performed on five lung sound data classes using multilayer perceptron as the classifier. | Paper Link |
| ACHMAD RIZAL, AHMAD ALFI ADZ DZIKRI, MUHAMMAD ARIK GERALDY FAUZI | Connectivity & Convergence Instruments for Smart Living | Classification of Normal and Abnormal Heart Sound using Continuous Wavelet Transform and ResNet-50 | Heart sound is the sound produced from the mechanical activity of the heart. Some researchers say the sound of the heart occurs from the opening and closing of the heart valve; some researchers say it occurs due to the eddy flow of blood in the heart chamber. Heart in a healthy condition produces certain heart sounds, while an unhealthy heart produces different heart sounds. Various studies have tried to develop a method for classifying heart sounds using digital signal processing methods. The proposed method generally consists of the feature extraction method and classifier. In this study, continuous wavelet transforms and residual neural network (ResNet-50) were used to classify normal and abnormal heart sounds. The lowest error-rate of 0.066 was achieved using 130x130 features. This result was quite competitive compared to previous research. The proposed method is ready to be tested on a dataset with more heart sounds abnormalities | Paper Link |
| ACHMAD RIZAL, ANITA MIFTAHUL MAGHFIROH, LILIEK SOETJIATIE, BAMBANG GURUH IRIANTO, TRIWIYANTO T, NURIL HIDAYANTI | Connectivity & Convergence Instruments for Smart Living | Improving Heart Rate Measurement Accuracy by Reducing Artifact Noise from Finger Sensors Using Digital Filters | Heart rate is an important indicator in the health sector that can be used as an effective and rapid evaluation to determine the health status of the body. Motion or noise artifacts, power line interference, low amplitude PPG, and signal noise are all issues that might arise when measuring heart rate. This study aims to develop a digital filter that reduces noise artifacts on the finger sensor to improve heart rate measurement accuracy. Adaptive LMS and Butterworth are the two types of digital filters used in this research. In this study, data were collected from the patient while he or she was calm and moving around. In this research, the Nellcor finger sensor was employed to assess the blood flow in the fingers. The heart rate sensor will detect any changes in heart rate, and the measurement results will be presented on a personal computer (PC) as signals and heart rate values. The results of this investigation showed that utilizing an adaptive LMS filter and a Butterworth low pass filter with a cut-off frequency of 6Hz, order 4, and a sampling frequency of 1000Hz, with the Butterworth filter producing the least error value of 7.57 and adaptive LMS maximum error value of 27.65 as predicted by the researcher to eliminate noise artifacts. This research could be applied to other healthcare equipment systems that are being monitored to increase patient measurement accuracy. | Paper Link |
| ACHMAD RIZAL, ATTIKA PUSPITASARI | Connectivity & Convergence Instruments for Smart Living | Lung Sound Classification Using Wavelet Transform And Entropy To Detect Lung Abnormality | Lung sounds contain necessary information about the health of the lungs and respiratory tract. They have unique and distinguishable pattern associated with the abnormalities probably occurred in the lungs or in the respiratory tract. Many researches have attempted to develop variety of methods to classify the lung sounds automatically. Of the methods, wavelet transform is one frequently used for physiological signals analysis. Commonly, the use of wavelet in feature extraction is used to break down the lung sounds into several sub-bands prior to the calculation of some parameters. In this research, five classes of lung sound that were obtained from various sources were used. Then, the wavelet analysis process were carried out using Discrete Wavelet Transform (DWT) and Wavelet Package Decomposition (WPD) analysis and entropy calculation as feature extraction. In DWT process, the highest accuracy obtained was 97.98% using Permutation Entropy (PE), Renyi Entropy (RE) and Spectral Entropy (SEN). Whereas in WPD, the highest accuracy produced is 98.99% by using eight sub-bands and RE. The results in this research are quite competitive if compared with the results of previous studies that used the wavelet method with the same datasets. | Paper Link |
| ACHMAD RIZAL, FAUZAN DIZKI ALIF AZMI, HILMAN FAUZI TRESNA SANIA PUTRA | Connectivity & Convergence Instruments for Smart Living | Obstructive Sleep Apnea (OSA) Classification Based on Heart Rate Variability (HRV) on Electrocardiogram (ECG) Signal Using Support Vector Machine (SVM) | Obstructive sleep apnea (OSA) is a disorder that occurs in a person's sleep pattern, and due to its ability to cause other health-related problems, various monitoring methods have been developed. One of the proposed methods is the ECG signal known as ECG-derived respiration (EDR), which uses changes in rhythm and patterns regularity to detect OSA occurrence. Therefore, this study aims to determine the OSA classification based on heart-rate variability (HRV) on electrocardiogram (ECG) signal using a support vector machine (SVM). The HRV parameter displays the rhythmic changes in the ECG signal under normal and OSA conditions. Eleven HRV characteristics were used to produce the highest accuracy of 89.5% with a support vector machine (SVM) as a classifier. The results were tested on a 1 minute long ECG signal annotated by an expert, which indicated that OSA can be detected by observing the dynamics of the distance of the R-R wave on the ECG signal. | Paper Link |
| ACHMAD RIZAL, ISTIQOMAH | Connectivity & Convergence Instruments for Smart Living | Lung Sounds Classification Based on Time Domain Features | Signal complexity in lung sounds is assumed to be able to differentiate and classify characteristic lung sound between normal and abnormal in most cases. Previous research has employed a variety of modification approaches to obtain lung sound features. In contrast to earlier research, time-domain features were used to extract features in lung sound classification. Electromyogram (EMG) signal analysis frequently employs this time-domain characteristic. Time-domain features are MAV, SSI, Var, RMS, LOG, WL, AAC, DASDV, and AFB. The benefit of this method is that it allows for direct feature extraction without the requirement for transformation. Several classifiers were used to examine five different types of lung sound data. The highest accuracy was 93.9 percent, obtained Using the decision tree with 9 types of time-domain features. The proposed method could extract features from lung sounds as an alternative. | Paper Link |
| ACHMAD RIZAL, RISANURI HIDAYAT, HANUNG ADI NUGROHO | Connectivity & Convergence Instruments for Smart Living | Modification of Grey Level Difference Matrix (GLDM) for Lung Sound Classification | Texture analysis is one of the methods to see the pixel variety in an image. Texture analysis can be done directly on image pixel value or done using transformation. Texture analysis can be utilized on the 1D signal to observe the variation of signal data samples. In this research, texture analysis using GLDM was modified as feature extraction method for lung sound classification. The features were classified using multilayer perceptron (MLP) and support vector machine (SVM) for performance evaluation. The result showed that modified GLDM with distance d = 10 achieved the highest accuracy of 94.9% using five GLDM's features, cubic SVM, and three-fold cross-validation. The result was achieved for five classes of lung sound consist of 99 data. The proposed indicated that texture analysis could be utilized for biological signal analysis, especially respiration sound. | Paper Link |
| ACHMAD RIZAL, VIVI ALIYAH PUTRI HANDZAH, PURBA DARU KUSUMA | Connectivity & Convergence Instruments for Smart Living | Heart Sounds Classification Using Short-Time Fourier Transform and Gray Level Difference Method | The heart sound coming from the patient is observed using a stethoscope, which is a medical tool to determine the patient's condition. The technique for this observation is called auscultation. This sound describes the condition of a person's heart. Because auscultation relies on the experience and knowledge of doctors, various methods for analyzing heart sounds are automatically developed by researchers. In this study, a method for classifying normal heart sounds and murmurs is proposed using the grey-level difference matrix (GLDM) feature taken from the short-time Fourier transform (STFT) plot. The STFT plot is converted into an image then the GLDM characteristics are calculated as input for the support vector machine as a classification. The experimental result shows that the highest accuracy of 83% is achieved using STFT 200-100 in four directions of GLDM. Even though this accuracy is not as high as the previous research, the proposed method is still open for exploration, such as distance selection in GLDM or other image analysis methods. | Paper Link |
| ACHMAD RIZAL, WAHMISARI PRIHARTI, DIEN RAHMAWATI, HUSNENI MUKHTAR | Connectivity & Convergence Instruments for Smart Living | Classification of Pulmonary Crackle and Normal Lung Sound using Spectrogram and Support Vector Machine | Crackles is one of the types of adventitious lung sound heard in patients with interstitial pulmonary fibrosis or cystic fibrosis. Pulmonary crackles of discontinuous short duration appear on inspiration, expiration, or both. To differentiate these pulmonary crackles, the medical staff usually uses a manual method, called auscultation. Various methods were developed to recognize pulmonary crackles and distinguish them from normal pulmonary sounds to be applied in digital signal processing technology. This paper demonstrates a feature extraction method to classify pulmonary crackle and normal lung sounds using Support Vector Machine (SVM) method using several kernels by performing spectrograms of the pulmonary sound to generate the frequency profile. Spectrograms with various resolutions and 3-fold cross-validation were used to divide the training data and the test data in the testing process. The resulting accuracy ranges from 81.4% - 100%. More accuracy values of 100% are generated by a feature extraction in several SVM kernels using 256 points FFT with three variations of windowing parameters compared to 512 points, where the best accuracy of 100% was produced by STFT-SVM method. This method has a potential to be used in the classification of other biomedical signals. The advantages of that are that the number of features produced is the same as the N-point FFT used for any signal length, the flexibility in the STFT parameters changes, such as the type of window and the window's length. In this study, only the Keiser window was tested with specific parameters. Exploration with different window types with various parameters is fascinating to do in further research. | Paper Link |
| ADNAN HASSAL FALAH, JONDRI | Connectivity & Convergence Instruments for Smart Living | Lung Sounds Classification Using Stacked Autoencoder and Support Vector Machine | Various methods have developed to analyze and classify many types of lung sound to reduce subjectivity in the auscultation procedure. In this paper, another proposed approach as a lung sound classification system has developed. This method combined a feature selection using unsupervised learning by stacked autoencoder (SAE) and support vector machine (SVM) as the classifier. Another feature extraction method using discrete wavelet transform also employed to bring a performance comparison to the proposed method. The result of this study showed that the proposed method scored 86.51%. | Paper Link |
| ALFI ZAHRA HAFIZHAH, SINUNG SUAKANTO, RISKA YANU FA'RIFAH, EDI TRIONO NURYATNO | Connectivity & Convergence Instruments for Smart Living | Prediction Model of Mortality with Respiratory Rate, Oxygen Saturation and Heart Rate using Logistic Regression | In the health context, sometimes we want to do an early warning of clinical deteriations. Because there are several incidents where people suddenly die without any noticeable symptoms. Or suddenly experience a drop without any obvious initial symptoms. Therefore, it is necessary to develop an initial study that can help to make predictions based on vital signs. But not all vital signs can be easily measured or in other words require the person to go to the hospital with complete equipment. It would be easier if people could make early warning predictions based on simple vital sign parameters that are respiratory rate, oxygen rate and heart rate. Where this vital sign parameter can be measured easily with existing tools without having to go to the hospital. This study aims to build a logistic regression model for predicting the mortality using oxygen saturation, respiratory rate and heart rate as parameters. Logistic regression is used because of the suitability of the model's advantages with the data, and the model evaluation uses F1- Macro. This study also uses Synthetic Minority Over-sampling technique and categorizing values of the variables to get a better model result. Training accuracy is 57%, while the evaluation accuracy is 55%. Although the accuracy is not yet good, this idea can be the basis for further development in developing early warning of clinical deterotions with limited parameters and can be done with measuring tools that are easily obtained without having to go to the hospital. | Paper Link |
| AMALYA CITRA PRADANA, ADIWIJAYA, ANNISA ADITSANIA | Connectivity & Convergence Instruments for Smart Living | Implementing binary particle swarm optimization and C4.5 decision tree for cancer detection based on microarray data classification | Cancer is one of deadly disease in the world and needed to detect the symptoms early. Cancer can be represented with microarray data with measuring the changes occured in gene expression level. Cancer detection can be done by doing classification technique for microarray data. One of most algorithm that applied for classification is C4.5 Decision Tree. It is a linier method which is easy to interpret and included into the algorithm which has given impact in classification but it is sensitive to noise data. Microarray data has a large features (high dimensional) which is not all the features has important information (high noise) and small samples which is causing the classification is difficult and affect the accuracy. Binary Particle Swarm Optimization (BPSO) is one of search optimization algorithm that could find the optimal feature. The purpose in this research consists of implementing and analysing the influence of feature selection and classification on microarray data using Binary Particle Swarm Optimization (BPSO) as feature selection and Decision Tree C4.5 as classifier. The discretization is needed for Decision Tree rule model and applied using K-Means. System is divided into two schemes such as Information Gain (IG) – C4.5 and BPSO – C4.5. The accuracy result based on IG – C4.5 and BPSO – C4.5 both are 54% and 99%. Applying feature selection before the classification could avoid the noise data in microarray data so it could form the rule accurately. With applying BPSO and Decision Tree is able to find the most significant feature and improve the accuracy. | Paper Link |
| ANDRIAN RAKHMATSYAH, AULIA ARIF WARDANA | Connectivity & Convergence Instruments for Smart Living | Detection Of Oxygen Levels (Spo2) And Heart Rate Using A Pulse Oximeter For Classification Of Hypoxemia Based On Fuzzy Logic | This study made the digital system to perform screening (early prediction) of Hypoxemia using MAX30102 sensor with the fuzzy value from SpO2 level and heart rate. This research also uses the Internet of Things (IoT) system to gather data from devices to the cloud. Hypoxemia is a lack of oxygen in the blood flowing in the body. Hypoxemia conditions in the body due to lack of oxygen levels in the blood will cause an increased heart rate. Hypoxemia conditions that are not immediately recognized cause damage to cells, tissues, and organs. Hypoxemia is an essential condition because information about oxygen levels in the blood is closely related to health conditions. In this project, researchers built a Hypoxemia early detection system. From the research results, it is found that the accuracy rate of the system to detect hypoxemia is 80%, with 60% sensitivity and 100% specificity. Based on the experiment, this research is able to help screening detection (early prediction) of Hypoxemia. | Paper Link |
| AULIA KHAMAS HEIKHMAKHTIAR, ABEBE TEKLE ABRHA, DA UN JEONG, KI MOO LIM | Connectivity & Convergence Instruments for Smart Living | Proarrhythmogenic Effect of the L532P and N588K KCNH2 Mutations in the Human Heart Using a 3D Electrophysiological Model | Atrial arrhythmia is a cardiac disorder caused by abnormal electrical signaling and transmission, which can result in atrial fibrillation and eventual death. Genetic defects in ion channels can cause myocardial repolarization disorders. Arrhythmia-associated gene mutations, including KCNH2 gene mutations, which are one of the most common genetic disorders, have been reported. This mutation causes abnormal QT intervals by a gain of function in the rapid delayed rectifier potassium channel (IKr). In this study, we demonstrated that mutations in the KCNH2 gene cause atrial arrhythmia. The N588K and L532P mutations were induced in the Courtemanche-Ramirez-Nattel (CRN) cell model, which was subjected to two-dimensional and three-dimensional simulations to compare the electrical conduction patterns of the wild-type and mutant-type genes. In contrast to the early self-termination of the wild-type conduction waveforms, the conduction waveform of the mutant-type retained the reentrant wave (N588K) and caused a spiral break-up, resulting in irregular wave generation (L532P). The present study confirmed that the KCNH2 gene mutation increases the vulnerability of the atrial tissue for arrhythmia. | Paper Link |
| BERLIAN MAULIDYA IZZATI, ASTI AMALIA NUR FAJRILLAH, RAHMAT FAUZI, WIDIA FEBRIYANI | Connectivity & Convergence Instruments for Smart Living | Pengembangan media interaktif dan implementasi aplikasi Mybidan sebagai upaya penurunan angka stunting | According to the report on the implementation of the Integration of the National Socio-Economic Survey (Susenas), the prevalence of stunting inthe Temanggung district is 25.79%, which is higher than the standard given by WHO, which is 20%. Stunting is a condition where toddlers have a length or height that is less than their age. The long-term impact of stunting is the disruption of physical, mental, intellectual,and cognitive development. In this regard, Telkom University in collaboration with the Temanggung DPPPAPPKB(Dinas Pemberdayaan Perempuan, Perlindungan Anak, Pengendalian Penduduk dan Keluarga Berencana)plans to increase community knowledge of stuntingand collaboration in supporting the Temanggung Regency government program by developing an interactive educational platform, as well as a collaboration platform that can facilitate communities and institutions to participate in helping prevent stunting in Temanggung Regency. This activity excepted tothat the implementation of this application can assist in increasing the effectiveness and efficiency of delivering messages and campaigns against stunting problems which is one of the programs belonging to the Temanggung DPPPAPPKB. | Paper Link |
| BRAHMANTYA AJI PRAMUDITA, ISTIQOMAH, ACHMAD RIZAL | Connectivity & Convergence Instruments for Smart Living | CRACKLE DETECTION IN LUNG SOUND USING STATISTICAL FEATURE OF VARIOGRAM | Pulmonary crackle sound is an adventitious lung sound that occurs due to several types of lung diseases such as pneumonia, pulmonary fibrosis, or chronic bronchitis. Crackle has distinctive sound patterns such as discontinuous, non-musical, and relatively short duration. Various methods were used to detect crackles in lung sounds such as entropy, wavelet-based methods, or spectral analysis. In this study, normal lung sound and pulmonary crackle sound classification were performed using the variogram as a feature extraction method. Modified variogram was applied to the pulmonary sound signal, and its statistical parameters were measured to distinguish crackle lung sound from normal lung sound. The experimental result produced the highest accuracy of 95.3% using Quadratic SVM as a classifier. These results indicated that the variogram could capture differences in signal dynamics in normal and pulmonary crackle sounds. | Paper Link |
| DEDY RAHMAN WIJAYA, MUHAMMAD AKBAR HAIKAL FRASANTA, DEDY RAHMAN WIJAYA, HERU NUGROHO, TORA FAHRUDIN | Connectivity & Convergence Instruments for Smart Living | Heart Diagnose Application Using Bagging Algorithm | One of the many organs in the human body is the heart. The function of the heart is to pump blood all over the body. If the heart is suffering damage or interference, it could cause many harms to people starting from chest pain, fatigue, dizziness, and the worse is death. To prevent this is by doing a heart health check to get the treatment needed. However, the patients have to come to the hospital to do a heart health check, which costs a lot of money. Therefore, we propose another method of diagnosing heart disease. This study uses a machine learning bagging algorithm (random forest) to detect heart disease with two classes: no disease or disease. The evaluation results show that the bagging algorithm achieved 97.8% accuracy from the best optimal grid search parameters. It can be concluded that this proposed method can fairly discriminate heart disease. | Paper Link |
| DINDA KARLIA DESTIANI, ADIWIJAYA, DODY QORI UTAMA | Connectivity & Convergence Instruments for Smart Living | Study of Wavelet and Line Search Techniques on Modified Backpropagation Polak-Ribiere Algorithm for Heart Failure Detection | Congestive Heart Failure (CHF) is a disease due to abnormalities in heart muscles so the heart not able to pump the bloods according to the body needs. Heart signals can be detected using Electrocardiography (ECG). However, there are no specific ECG features of CHF patients, whereas the extracted features of ECG signals play a significant role for diagnosing the cardiac disease. In this paper, we used Discrete Wavelet Transform (DWT) and Wavelet Package Decomposition (DWT) to extract the features. As for the process of this work is divided into three phases, i.e. pre-processing, feature extraction, and classification. Thus, the extracted features will then be used as inputs for the classification system we used; Artificial Neural Network (ANN) Modified Backpropagation (MBP) Polak-Ribiere Conjugate Gradient with line search technique. At the end of the study, the feature was obtained using WPD at 5 th level with 22 records of training data. Gained an average value that is higher than the other trials, 72.5%. For the classification, known that 30 neurons in hidden layer and Charalambous' Search is the fastest search technique to be applied to this case with processing time 2.65 seconds, 14 epochs, and 87.5% accuracy. | Paper Link |
| FHIRA NHITA, ISMAN KURNIAWAN | Connectivity & Convergence Instruments for Smart Living | Classification of Non-Small Cell Lung Cancer Based on Gene Expression in Cases of Smokers and Non-Smokers using Ensemble Methods with Statistical based Feature Selection | Lung cancer is one of the leading causes of death globally. One of the main risk factors for lung can ceris smoking, which causes more than 90% of lung cancer cases. There are two types of lung cancer, i.e., Small Cell Lung Cancer (SCLC) and Non-Small Cell Lung Cancer (NSCLC), which the latter is the most common. One method that can be used to detect cancer is the implementation of machine learning on gene expression data. Machine learning is one approach that promises good performance in classifying gene expression data. This study aimed to predict the existence of NSCLC based on gene expression, whether including NSCLC or normal. We used three data sets, i.e., GSE10072, GSE19804, and GSE19188, which relate to the cases of NSCLC in smokers and nonsmokers. The prediction was carried out using six Ensemble Methods, i.e., Random Forest, Adaptive Boosting, Extra Tree, Gradient Boosting, Extreme Gradient Boosting, and Categorical Boosting. Feature selection was carried out by calculating the correlation between feature and target according to statistical parameters, i.e., ANOVA, Mutual Information (MI), and a combination of ANOVA and MI. We obtained the prediction model that outperformed the related studies for two similar datasets with the value of accuracy for the GSE10072, GSE19804, and GSE19188 datasets 100%, 97.22%, and 100%, respectively | Paper Link |
| FITYANUL AKHYAR, LEDYA NOVAMIZANTI, RAIHAN ARFI MAULANA, CHI-WEN LUNG, CHIH-YANG LIN | Connectivity & Convergence Instruments for Smart Living | Reinforced Cascading Convolutional Neural Networks and Vision Transformer for Lung Disease Diagnosis | Lung diseases are among the most deadly infectious diseases worldwide. Covid-19 infection is a current disease that falls within this category and has impacted public health in countries across the globe. Accordingly, this study focuses on building a lung disease identification system using a state-of-the-art deep cascade learning classification model, EfficientNet-Vision Transformer. The proposed Real ESRGAN is utilized to enhance the input of EfficientNet, while image Relative Position Encoding (iRPE) is added to improve the attention of the transformer network. Moreover, weight balancing is applied to stabilize the performance of the proposed system. When trained on the X-Ray dataset, our model achieved 93.757% accuracy on five classes of lung disease: Normal, Covid-19, Viral Pneumonia, Bacterial Pneumonia, and Tuberculosis. | Paper Link |
| HESTY SUSANTI | Connectivity & Convergence Instruments for Smart Living | Pleural LineDetectionEnhancement in LungUltrasonography(LUS) Based on Morphological and Adaptive Structural 2DFilter | Lung ultrasonography (LUS) imaging has been used intensively to investigate and assess the lung’s various pathological conditions. A diagnostic system of lung abnormalities is developed to detect and localize the pleural line that can be viewed as the artifacts in LUS image. The continuous pleural line indicates one crucial pattern of a healthy lung. The regular repeated horizontal A-line marks this pattern with a fixed distance between the lines and ideally, produces a higher contrast in the lung image. This work proposes an image processing framework for enhancing pleural line detection in healt ung ultrasonography (LUS) imaging has been used intensively to investigate and assess the lung’s various pathological conditions. A diagnostic system of lung abnormalities is developed to detect and localize the pleural line that can be viewed as the artifacts in LUS image. The continuous pleural line indicates one crucial pattern of a healthy lung. The regular repeated horizontal A-line marks this pattern with a fixed distance between the lines and ideally, produces a higher contrast in the lung image. This work proposes an image processing framework for enhancing pleural line detection in healthy subjects and patients as an early stage of further lung image interpretations in pneumonia patients. The proposed image processing framework is based on a top-hat morphological grayscale 2D filter with a texture structure element and an adaptive structural 2D low pass filter. This framework is evaluated for open dataset video ultrasonography (USG) of Point-of-care ultrasound (POCUS) to enhance the pleural line detection for typical video LUS acquired using a linear and a convex transducer. hy subjects and patients as an early stage of further lung image interpretations in pneumonia patients. The proposed image processing framework is based on a top-hat morphological grayscale 2D filter with a texture structure element and an adaptive structural 2D low pass filter. This framework is evaluated for open dataset video ultrasonography (USG) of Point-of-care ultrasound (POCUS) to enhance the pleural line detection for typical video LUS acquired using a linear and a convex transducer. | Paper Link |
| HUSNENI MUKHTAR, WILLY ANUGRAH CAHYADI, DIEN RAHMAWATI, TEUKU ZULKARNAIN MUTTAQIEN, YOGA PUJIRAHARJO, KUSNAHADI SUSANTO, SHEIZI PRISTA SARI, EKA AFRIMA SARI, OOY ARIE SUDIYONO | Connectivity & Convergence Instruments for Smart Living | E-Growth Monitoring System (EGMS) sebagai Upaya Penurunan Prevalensi Stunting | Indonesian government, through National Strategy for Preventing Child Stunting 2018-2024, prioritize both prevention and reduction of stunting prevalence by targeting the causes. It requires inter-sector coordinations between government, private companies, businesses, and citizens, especially in local clinics. E-Growth Monitoring System (EGMS) is proposed to measure the growth of infant and toddler, specifically to detect a possible stunting and bad nutrition as early as possible in order to devise an improvement effectively. The implementation of this innovation employs the use of both ultrasonic sensor and load cell to measure the body height and weight of infant and toddler. Its expected margin of error for the sensors are 0.01 – 4.36% and 0.00 – 1.43% for ultrasonic sensor and load cell, respectively | Paper Link |
| INDRARINI DYAH IRAWATI, SUGONDO HADIYOSO, ARFIANTO FAHMI | Connectivity & Convergence Instruments for Smart Living | Compressive Sensing in Lung Cancer Images for Telemedicine Application | Telemedicine technology as a solution to prevent the spread of Covid-19. Tele-radiology for lung cancer images requires a large bandwidth when the image is transmitted, whereas the available bandwidth is limited. CT-scan lung cancer image has a very large capacity so that it requires a large storage space, while the storage capacity is very limited. On the sender side, the application of compressive sensing as an alternative solution to obtain data compression with a high compression ratio but requires high accuracy on the receiver. In addition to make it easier for medical staff and doctor for diagnosing the type of lung cancer, the recipient requires a lung cancer image classification, which consists of 3 types of cancer, including: adeno carcinoma (ACA), squamous cell carcinoma (SCC), and benign lung cancer (N). This paper proposes a combination method consisting of a Compressive Sensing (CS) algorithm, feature extraction, and KNN classification that can work effectively and efficiently in telemedicine applications. The results showed that CS worked effectively for compression with large compression ratios without having an influence on the accuracy results. The sparse technique FFT provides the highest accuracy compared to IFFT, DWT and without sparsing. The classification using KNN shows that the N image has uniquely extracted characteristics and give accuracy up to 100%, whereas the image of ACA and SCC provide accuracy by 70%. | Paper Link |
| INDRARINI DYAH IRAWATI, SUGONDO HADIYOSO, GELAR BUDIMAN, ARFIANTO FAHMI | Connectivity & Convergence Instruments for Smart Living | A Novel Texture Extraction Based Compressive Sensing for Lung Cancer Classification | Lung cancer images require large memory storage and transmission bandwidth for sending the data. Compressive sensing (CS), as a method with a statistical approach in signal sampling, provides different output patterns based on information sources. Thus, it can be considered that CS can be used for feature extraction of compressed information. In this study, we proposed a novel texture extraction-based CS for lung cancer classification. We classify three types of lung cancer, including adenocarcinoma (ACA), squamous cell carcinoma (SCC), and benign lung cancer (N). The classification is carried out based on texture extraction, which is processed in 2 stages, the first stage to detect N and the second to detect ACA and SCC. The simulation results show that two-stage texture extraction can improve accuracy by an average of 84%. The proposed system is expected to be decision support in assisting clinical diagnosis. In terms of technical storage, this system can save memory resources. The proposed two-step texture extraction system combined with CS and K- Nearest Neighbor has succeeded in classifying lung cancer with high accuracy; the system can also save memory storage. It is necessary to examine the complexity of the proposed method so that it can be analyzed further. | Paper Link |
| INUNG WIJAYANTO SUGONDO HADIYOSO | Connectivity & Convergence Instruments for Smart Living | Electrooculogram (EOG) based Mouse Cursor Controller Using the Continuous Wavelet Transform and Statistic Features | This study design a system prototype to control a mouse cursor's movement on a computer using an electrooculogram (EOG) signal. The EOG signal generated from eye movement was processed utilizing a microcontroller with an analog to the digital conversion process, which communicates with the computer through a USB port. The signal was decomposed using continuous wavelet transform (CWT), followed by feature extraction processes using statistic calculation, and then classified using K-Nearest Neighbors (k-NN) to decide the movement and direction of the mouse cursor. The test was carried out with 110 EOG signals then separated, 0.5 as training data and 0.5 as test data with eight categories of directional movement patterns, including up, bottom, right, left, top right, top left, bottom right bottom left. The highest accuracy that can be achieved using CWT-bump and kurtosis is 100%, while the time needed to translate the eye movement to the cursor movement is 1.9792 seconds. It is hoped that the proposed system can help assistive devices, particularly for Amyotrophic Lateral Sclerosis (ALS) sufferers. | Paper Link |
| MAULANA AKBAR DWIJAYA, RUDI PURWO WIJAYANTO, RATNA ASTUTI NUGRAHAENI | Connectivity & Convergence Instruments for Smart Living | Model Design Of The Image Recognition Of Lung Ct Scan For Covid-19 Detection Using Artificial Neural Network | COVID-19 has become a pandemic and is a big problem that needs to be checked out immediately. CT scan images can explain the lung conditions of COVID-19 patients and have the potential to be a clinical diagnostic tool. In this research, we classify COVID-19 by recognizing images on a computer tomography scan (CT scan) of the lungs using digital image processing and GLCM feature extraction techniques to obtain grayscale level values in CT images, followed by the creation of an artificial neural network model. So that the model can classify CT scan images, the results in this research obtained the most optimal model for COVID-19 classification performance with 90% accuracy, 88% precision, 91% recall, and 90% F1 score. This research can be a useful tool for clinical practitioners and radiologists to assist them in the diagnosis, quantification, and follow-up of COVID-19 cases. | Paper Link |
| MUHAMMAD ADNAN PRAMUDITO, R YUNENDAH NUR FU'ADAH, RITA MAGDALENA, ACHMAD RIZAL, FAUZI FRAHMA TALININGSIH | Connectivity & Convergence Instruments for Smart Living | Ecg Signal Processing Using 1-D Convolutional Neural Network For Congestive Heart Failure Identification | Heart disease is one of the leading causes of death in the world. Congestive Heart Failure (CHF) is one type of heart disease that needs attention. CHF is a condition in which the heart cannot pump blood adequately throughout the body. This disease usually affects patients over the age of 60 years. An EKG can be used to diagnose this condition. However, doctors need to diagnose manually, namely, reading the ECG signal directly. Therefore, this study aims to create a system that can diagnose CHF automatically using the 1D convolutional neural network (CNN) method. This CNN 1D method uses normalization as preprocessing, three hidden layers with 16 output channels, a fully connected layer, and sigmoid activation. The research dataset comes from MIT-BIH and BIDMC. Based on this study, 100% accuracy results were obtained with recall, precision, and 1 F1-Score, respectively, so this study can assist medical staff in identifying CHF conditions and providing appropriate therapy to patients. | Paper Link |
| MUHAMMAD ALIF AKBAR, SATRIA MANDALA | Connectivity & Convergence Instruments for Smart Living | IoT on Heart Arrhythmia Real Time Monitoring | Heart monitoring is popular in the recent 5 yearns. We can see this with emergence of variouscardiovascular monitoring products based on wearable sensors. Those products commonlycommunicates using radio telemetry which has expensive operational costs. Some researchtry to implement internet of things (IoT) concept to solve the issue. However, those IoTimplementation aren’t efficient enough. The research are only focused on how to read thesensors data and allowing it to be monitored on real-time. This research proposed a cloudbased IoT architecture to monitor arrhythmia, one type of a common heart attack, which moreefficient than previous research. Arrhythmia detector that used in this paper is an improvementof algorithm proposed by Tsipouras et al, which using R peak on ECG. The system proposedon this paper has been tested using MIT-BIH datasets and has result 93.11% accuracy against3 arrhythmia class, that is PAC, PVC and VT. The interesting result is that by implementingIoT, the R-Peak detection algorithm’s execution time decreased up to 30% compared to hasbeen proposed by Pan and Tompkins. The average of execution time of every sample isdecreased to 0.00749 ms. | Paper Link |
| MUHAMMAD FADHIL IHSAN, SATRIA MANDALA, MIFTAH PRAMUDYO | Connectivity & Convergence Instruments for Smart Living | Study of Feature Extraction Algorithms on Photoplethysmography (PPG) Signals to Detect Coronary Heart Disease | Coronary Heart Disease (CHD) is the most dangerous heart disease, this disease occurs, when the blood supply containing oxygen and nutrients to the heart muscle blocked by plaque in the heart blood vessels or coronary arteries. Currently, there are many ways of diagnosing coronary heart disease, starting from using ECG to Cardiac catheterization. However, it has some drawbacks, including the inflexibility of diagnosing quickly and invasive procedures. Heart rate variability (HRV) is a strong indication of cardiovascular diseases; as a result, any change in the normal heart rate (or blood volume) activity is a major marker for a potential cardiovascular malfunction. Through a series of waves and peak detection, photoplethysmography (PPG) detects blood pressure, oxygen saturation, and cardiac output. In recent years, there have been more studies using ECG signals to detect CHD compared to PPG signals, especially those discussing feature extraction on PPG signals in detecting CHD because this greatly affects the accuracy of CHD detection. In this study, proposed a literature study of feature extraction algorithm for detecting coronary heart disease using photoplethysmography. For the feature extraction, three algorithm will be discussed are respiratory rate (RR) interval, HRV Features and Time Domain Features. HRV features, with 94.4% accuracy, 100% sensitivity, and 90.9% specificity, is the best feature extraction approach of the three proposed techniques using decision tree classifier. | Paper Link |
| MUHAMMAD IKHSAN SANI, GIVA ANDRIANA MUTIARA, RADEN SRI DEWANTO WIJAYA PUTRA | Connectivity & Convergence Instruments for Smart Living | fit-NES: Wearable Bracelet for Heart Rate Monitoring | The heart is a vital organ that serves to pump blood to the whole body. A heart rate can be used as a healthy body parameter conditions. Growing evidence suggests that IT-based health records play essential role to drive medical revolution especially on data storage and processing. The heart rate measurement (HRM) process usually involves wearable sensor devices to record patient’s data. This data is recorded to help the doctors to analyze and provide a better diagnose in order to determine the best treatment for the patients. Connecting the sensor system through a wireless network to a cloud server will enable the doctor to monitor remotely. This paper presents fit-NES wearable bracelet, an alternative method for integrating a HR measurement device using optical based pulse sensor and Bluetooth-based communication module. This paper is also present the benchmarking of proposed system with several various commercial HR measurement devices. | Paper Link |
| MUHAMMAD ILHAM ALHARI, WIDIA FEBRIYANI, WADER TRISEPA JONSON, ASTI AMALIA NUR FAJRILLAH | Connectivity & Convergence Instruments for Smart Living | Perancangan Smart Village Platform Aplikasi Edukatif untuk Pengentasan Stunting serta Monitoring Kesehatan Ibu Hamil | The era of globalization is closely related to comprehensive technological developments in various fields. Digitalization in all sectors is undeniable, currently, digitalization is not only developing in cities, but several villages in Indonesia are starting to develop and build digital villages, known as smart villages. However, this has not developed optimally. Therefore, it is necessary to develop a smart village, especially in the health sector. In this study, a platform was designed that can be used to educate with the help of an application platform. The method used in this study was to conduct a survey with related parties and provide education on village programs to monitor the stunting of pregnant women and toddlers. In addition, the authors also held discussions to help the community. The hope of the educational platform related to stunting reduction is to support a national program to reduce stunting rates in Indonesia. | Paper Link |
| MUHAMMAD RIZQY ALFARISI | Connectivity & Convergence Instruments for Smart Living | Ubiquitous Electronic Health System - Rancang Bangun Smart Mouse Dan Smart Watch Pengukur Denyut Jantung Dan Suhu Tubuh | The purpose of this research is to design a Ubiquitous Electronic Health System to monitor health during activities. The Ubiquitous Electronic Health System is a health monitoring system and facilitator of human health support devices that are applied to devices commonly used in everyday life such as mirrors, chairs, computer mice, watches, mobile phones, and others by utilizing the Photoplethysmograph method. The equipment developed in this study consisted of a computer mouse and a watch that was added with a photodiode, infrared, and a DS18b20 sensor with functionality as a heart rate detector and body temperature measurement. into the body but the measurement or screening action is carried out with the help of sensors attached to the skin, measurements are carried out in real-time when the equipment is used daily, the measurement results can be seen on the mobile phone screen and desktop applications, the data obtained from the measurement results can then be sent to the server to be stored as a user's medical record which can be used by the user to carry out further examinations to the doctor. The parameters that are the points in this study are the number of heart beats per minute and the measurement of body temperature, these two parameters are tested by comparing the results of tests carried out by tools designed with oximeters and thermometers. The test results from the Ubiquitous Electronic Health System tool provide an accuracy of up to 98% for measuring heart rate and 85% for measuring body temperature. Design and schematic have been shown in this study. | Paper Link |
| OTNIEL ABIEZER, FHIRA NHITA, ISMAN KURNIAWAN | Connectivity & Convergence Instruments for Smart Living | Identification of Lung Cancer in Smoker Person Using Ensemble Methods Based on Gene Expression Data | Cancer is a symptom of abnormal cell growth and is uncontrollable. Lung cancer is one of the most common types of cancer. Smoking is the leading cause of lung cancer. Early detection is essential because it can prevent lung cancer and get the proper treatment, such as a low-dose CT scan (LDCT). However, this effort still has drawbacks. With advances in DNA microarray technology, it is possible to measure the gene expression level of thousands of genes or cells in each tissue. The identification of lung cancer can be made using machine learning from the gene expression data (DNA microarray). In this study, a machine learning prediction model has been built using the Ensemble Methods, i.e. Random Forest and AdaBoost. The best model is Random Forest with 900 features and gets 0.77 for accuracy score and 0.80 for f1 score. | Paper Link |
| PATRIK GUNTI PRATAMA, DEDY RAHMAN WIJAYA, HERU NUGROHO, RATHIMALA KANNAN | Connectivity & Convergence Instruments for Smart Living | Boosting Algorithm for Classifying Heart Disease Diagnose | The heart is a component of the human body that is responsible for pumping blood and distributing oxygen throughout the body. Hospitals and doctors are still checking heart disease diagnoses manually at this time. However, this method is expensive and time-consuming. In this study, the Gradient Tree Boosting (GTB) algorithm was used to detect patients diagnosed with heart disease (disease and no disease). The purpose of the method is to provide convenience to obtain early information on heart health. With the dataset provided from the UCI Machine Learning Repository, there are 13 supporting features to detect heart disease with a total of 304 data. This study uses the GTB model with the best four parameters and utilizes feature selection which is used to classify. From the results of the study to get a recall score of 0.98, the proposed method succeeded in classifying patients who were diagnosed with heart disease correctly. | Paper Link |
| R YUNENDAH NUR FU'ADAH | Connectivity & Convergence Instruments for Smart Living | An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine Learning | Heart-sound auscultation is one of the most widely used approaches for detecting cardiovascular disorders. Diagnosing abnormalities of heart sound using a stethoscope depends on the physician’s skill and judgment. Several studies have shown promising results in automatically detecting cardiovascular disorders based on heart-sound signals. However, the accuracy performance needs to be enhanced as automated heart-sound classification aids in the early detection and prevention of the dangerous effects of cardiovascular problems. In this study, an optimal heart-sound classification method based on machine learning technologies for cardiovascular disease prediction is performed. It consists of three steps: pre-processing that sets the 5 s duration of the PhysioNet Challenge 2016 and 2022 datasets, feature extraction using Mel frequency cepstrum coefficients (MFCC), and classification using grid search for hyperparameter tuning of several classifier algorithms including k-nearest neighbor (K-NN), random forest (RF), artificial neural network (ANN), and support vector machine (SVM). The five-fold cross-validation was used to evaluate the performance of the proposed method. The best model obtained classification accuracy of 95.78% and 76.31%, which was assessed using PhysioNet Challenge 2016 and 2022, respectively. The findings demonstrate that the suggested approach obtained excellent classification results using PhysioNet Challenge 2016 and showed promising results using PhysioNet Challenge 2022. Therefore, the proposed method has been potentially developed as an additional tool to facilitate the medical practitioner in diagnosing the abnormality of the heart sound. | Paper Link |
| R YUNENDAH NUR FU'ADAH, KI MOO LIM | Connectivity & Convergence Instruments for Smart Living | Classification of Atrial Fibrillation and Congestive Heart Failure Using Convolutional Neural Network with Electrocardiogram | Atrial fibrillation (AF) and congestive heart failure (CHF) are the most prevalent types of cardiovascular disorders as the leading cause of death due to delayed diagnosis. Early diagnosis of these cardiac conditions is possible by manually analyzing electrocardiogram (ECG) signals. However, manual diagnosis is complex, owing to the various characteristics of ECG signals. An accurate classification system for AF and CHF has the potential to save patient lives. Therefore, this study proposed an ECG signal classification system for AF and CHF using a one-dimensional convolutional neural network (1-D CNN) to provide a robust classification system performance. This study used ECG signal recording of AF, CHF, and NSR, which can be accessed on the Physionet website. A total of 5600 ECG signal segments were obtained from 56 subjects, divided into train sets from 42 subjects (N = 4200 ECG segments), and test sets from 14 subjects (N = 1400). We applied for leave-one-out cross-validation in training to select the best model. The proposed 1-D CNN algorithm successfully classified raw data of ECG signals into normal sinus rhythm (NSR), AF, and CHF by providing the highest classification accuracy of 99.643%, f1-score, recall, and precision of 0.996, respectively, with an AUC score of 0.999. The results showed that the proposed method extracted the ECG signal information directly without needing several preprocessing steps and feature extraction methods that potentially reduce the information contained in the ECG signals. Furthermore, the proposed method outperformed previous studies in classifying AF, CHF, and NSR. Therefore, this approach can be considered as an adjunct for medical personnel to diagnose AF, CHF, and NSR. | Paper Link |
| RATNA KOMALASARI, ACHMAD RIZAL, FIKY YOSEF SURATMAN | Connectivity & Convergence Instruments for Smart Living | Classification of Normal and Murmur Hearts Sound using the Fractal Method | Heart sounds contain important information related to heart health. Normal heart sounds produce sound patterns that are different from abnormal ones. Various digital signal processing methods have been used to differentiate these heart sound signals and the most usual methods are wavelet analysis, entropy analysis, or a combination of both. In this study, the fractal dimension was used to classify normal heart sounds and murmur sounds by using the box-counting fractal dimension (BCFD), Katz fractal dimension (KFD), Sevcik fractal dimension (SFD), and Higuchi fractal dimension (HFD) as the heart sound features. The highest accuracy reached 100% using SFD as a feature and KNN as a classifier. These results were tested on 50 normal heart sounds and 50 heart sounds murmurs with 3-fold cross-validation. The results showed that choosing the right fractal dimension can distinguish normal heart sounds and murmurs. | Paper Link |
| REZA RENDIAN SEPTIAWAN | Connectivity & Convergence Instruments for Smart Living | A comparison on the use of Perlin-noise and Gaussian noise based augmentation on X-ray classification of lung cancer patient | The use of deep learning in medical image classification has become an important study in the past few years. The proper use of this method, however, is still hindered by many problems, one of it being the imbalance of dataset available for training which resulted in small-set database. In this study, the effect of noise-based augmentation on the performance of deep learning based classification will be studied. The noises which were used for the augmentation method were Perlin-noise and Gaussian noise. The modality of medical image used in this study is X-ray. 174 X-ray images (87 cancer, 87 normal) were used in this study and will be classified by using transfer learning from previously trained deep learning architecture. The deep learning architecture used was vgg-19. The images were divided into two groups, 138 (69 cancer, 69 normal) images were used for training phase and 36 (18 cancer, 18 normal) were images used for testing phase. Three deep learning models were used for the classification tasks, the first one was retrained to classify the original images, the second one was retrained by using mix of original images and images with Perlin-noise, and the third one was retrained by using mix of original images and images with Gaussian noise. The results showed that the three models returned similar accuracy of 0.8 which indicate that the use of noise-based augmentation can increase the performance of deep learning in classifying medical images with small set training database. | Paper Link |
| SATRIA MANDALA, MUHAMMAD FARHAN | Connectivity & Convergence Instruments for Smart Living | Detecting Heart Valve Disease Using Support Vector Machine Algorithm Based On Phonocardiogram Signal | Valvular Heart Disease (VHD) is a type of heart valve disease that is triggered by a disorder or abnormality of one or more of the four hearts that makes it difficult for blood to flow into the next chamber or blood vessel, or vice versa. In recent years, many methods have been proposed to detect the occurrence of VHD. With advances in technology to detect these abnormalities can use telemedicine technology. This paper analyzes the PCG signal (Phonocardiogram) from the patient. There are 3 stages in detecting VHD, namely denoising, feature extraction, and PCG signal classification. The accuracy value obtained from the whole detection process can change and be influenced by the results of the classification algorithm and hyperparameter. Therefore, the selection of the right hyperparameter is important. Of the many pieces of literature that propose VHD detection. To solve the above problems, this research proposes the development of a classification algorithm that supports the improvement of VHD detection accuracy. In addition, prototypes based on the proposed algorithm will also be developed. This research also analyzes the accuracy of the proposed prototype detection. The methods used in this research are 1. Literature study on VHD detection, 2. STFT Denoising, 3. MFCC Feature Extraction, 4. SVM classification algorithm development, 5. Evaluation, 6. Tune SVM algorithm to get higher score. The performance test results show that the proposed algorithm has achieved an average accuracy of 99.5%%, F1 Score is 99%, recall is 99%, precision 100%. | Paper Link |
| SATRIA MANDALA, WINO RAMA PUTRA, SATRIA MANDALA, MIFTAH PRAMUDYO | Connectivity & Convergence Instruments for Smart Living | Study Of Feature Extraction Methods To Detect Valvular Heart Disease (Vhd) Using A Phonocardiogram | Valvular Heart Disease (VHD) is a type of heart disease that is triggered by a failure or abnormality in one or more of the four heart valves which results in difficulty in circulating blood between the chambers or blood vessels of the heart. In recent years, many methods have been proposed to detect occurrence of VHD. With advances in technology, to detect these abnormalities can utilize telemedicine technology. The detection method in this paper analyzes the PCG signal (Phonocardiogram) from the patient. The performance value obtained from the detection process is strongly influenced by the algorithm at the feature extraction stage and the feature selection method. Therefore, the selection of the right feature extraction and feature selection method is important. Of the many literatures that propose detection of VHD with the application of feature extraction methods, the average performance obtained is still low. To solve the above problems, this research proposes the development of a feature extraction algorithm that supports the improvement of VHD detection accuracy. In addition, prototypes based on the proposed algorithms and methods were also developed. This research also analyzes the accuracy of the proposed prototype detection. The methods used in this research are 1. Literature study on VHD detection, 2. Development of feature extraction algorithms methods, 3. Performance testing and analysis. The performance test results show that the proposed algorithm has achieved an average accuracy of 99%, sensitivity of 100% and specificity of 97%. | Paper Link |
| SUGONDO HADIYOSO, ACHMAD RIZAL | Connectivity & Convergence Instruments for Smart Living | Empirical Mode Decomposition and Grey Level Difference for Lung Sound Classification | Lung sound is one of the parameters of respiratory health. This sound has a specific character if there is a disease in the lungs. In some cases, it is difficult to distinguish one type of lung sound to another. It takes the expertise, experience and sensitivity of clinicians to avoid misdiagnosis. Therefore, many studies have proposed a feature extraction method combined with automatic classification method for the detection of lung disease through lung sound analysis. Since the complex nature of biological signals which are produced by complex processes, the multiscale method is an interesting feature extraction method to be developed. This study proposes an empirical mode decomposition (EMD) and a modified gray level difference (GLD) for a lung sound classification. The EMD was used to decompose the signal, and then GLD was measured on each decomposed signal as a feature set. There are five classes of lung sounds which were simulated in this study, including normal, wheeze, crackle, pleural rub, and stridor. Performance evaluation was carried out using a multilayer perceptron (MLP) and 3-fold cross-validation. This proposed method yielded the highest accuracy of 96.97%. This study outperformed several previous studies which were simulated on the same dataset. It is hoped that in the future, the proposed methods can be tested on larger datasets to determine the robustness of the methods. | Paper Link |
| SUGONDO HADIYOSO, AKHMAD ALFARUQ, YUYUN SITI ROHMAH, ROHMAT TULLOH | Connectivity & Convergence Instruments for Smart Living | Sistem Pengukur Tekanan Darah Secara Online untuk Aplikasi Remote Monitoring Kesehatan Jantung | This research focuses on an online blood pressure measuring system that can be accessed through an Android application on a smart phone. The system that is implemented consists of Blood Pressure meter modules, microcontrollers, Bluetooth modules and cloud server applications. The microcontroller will read the blood pressure value (systolic/diastolic) and the heart pulse then sends the data to a smart phone via Bluetooth using UART protocol to be forwarded to the cloud server. Users who have interests and authorizations can access the data online through the iHealth application installed on a smart phone. From the test results, the blood pressure measuring system has an error tolerance value of 2-5 mmHg. The maximum distance of sending data via Bluetooth is 10 meters. The iHealth application runs on the Android version of a minimum of Ice Cream Sandwich with a 23MB RAM memory requirement. | Paper Link |
| SULHIJAS ZAINUDDIN, FHIRA NHITA, UNTARI NOVIA WISESTY | Connectivity & Convergence Instruments for Smart Living | Classification of Gene Expressions of Lung Cancer and Colon Tumor Using Adaptive-Network-based Fuzzy Inference System (ANFIS) with Ant Colony Optimization (ACO) as a Feature Selection | Cancer is one of the death causes in most countries. In 2015 the death count caused by cancer is reaching 8,8 million and in 2030 it is estimated that the death count reaches 13 million. Therefore, in this research conducted an expression classification of gene using Adaptive-Network-based Fuzzy Inference System (ANFIS) with Ant Colony Optimization (ACO) as the feature selection can help the process of early diagnosis to reduce mortality. The data used are colon tumor and lung cancer obtained from Kent Ridge Biomedical Data Set Repository. Accuracy results obtained are influenced by several factors such as data partition method, the number of ants, and the number of gene attributes. The best accuracy results obtained for colon tumor is 94,73% and lung data cancer is 100%. | Paper Link |
| TASYA NURFAUZIAH RAMADHANI ROHIMAT, FHIRA NHITA, ISMAN KURNIAWAN | Connectivity & Convergence Instruments for Smart Living | Implementation of Genetic Algorithm-Support Vector Machine on Gene Expression Data in Identification of Non-Small Cell Lung Cancer in Nonsmoking Female | Lung cancer is the leading cause of death in the world. There are two types of lung cancer, i.e., non-small cell lung cancer and small cell lung cancer. The major cause of lung cancer is smoking. However, there are several cases of non-small lung cancer, with 7% of women with lung cancer in Taiwan having a smoking history. Early detection of cancer will help it go faster and save lives every year. Nowadays, the technology being used is very helpful in the medical field because it uses microarray technology which can help detect cancer in the early phase by analyzing DNA and RNA. In this study, we utilized GA combined with SVM for the classification of Non-Small Cell Lung Cancer in a non-smoking female with microarray data. Hyperparameter tuning is performed to improve model performance. We discovered that SVM with a linear kernel performs better than alternative kernels with accuracy and F1-score values of 0.91 and 0.91, respectively. | Paper Link |
| UNANG SUNARYA, LYRA VEGA UGI | Connectivity & Convergence Instruments for Smart Living | Analisis Fitur Domain Waktu ECG Heart Rate Variability Berdasarkan Gain Informasi | One of the most frequent methods to detect heart-related diseases is by calculating the ratio of total sleep and awake of someone during night sleep. However, it is often encountered problems either in data acquisition or data processing to output the results of the analysis. One of the reasons is selecting ECG features improperly during implementation. Therefore, this study has been conducted ECG features selection in the time domain and classification of sleep and awake states across 10 subjects based on Heart Rate Variability (HRV) features obtained using a random forest algorithm. Wavelet was used to get the proper ECG signal components while information gain was used to select the dominant ECG features. The implementation results showed an average accuracy of 80.26 % with the median nearest neighbor index as the best feature. | Paper Link |
| UNTARI NOVIA WISESTY, UNTARI NOVIA WISESTY, AYU PURWARIANTI, ADI PANCORO, AMRITA CHATTOPADHYAY, NAM NHUT PHAN, ERIC Y. CHUANG, TATI RAJAB MENGKO | Connectivity & Convergence Instruments for Smart Living | Join Classifier Of Type And Index Mutation On Lung Cancer Dna Using Sequential Labeling Model | The sequential labeling model is commonly used for time series or sequence data where each instance label is classified using previous instance label. In this work, a sequential labeling model is proposed as a new approach to detect the type and index mutations simultaneously, using DNA sequences from lung cancer study cases. The methods used are One Dimensional Convolutional Neural Network (1D-CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Bidirectional Gated Recurrent Unit (Bi-GRU). Each nucleotide in the patient’s DNA sequence is classified as either normal or with a certain type of mutation in which case, its index mutation is predicted. The mutation types detected are either substitution, insertion, deletion, or delins (deletion insertion) mutations. Based on the experiments that were conducted using EGFR gene, BiLSTM and Bi-GRU displayed better performance and were more stable than 1D-CNN. Further tests were carried out on the TP53 , KRAS , CTNNB1 , SMARCA4 , CDKN2A , PTPRD , BRAF , ERBB2 , and PTPRT gene. The proposed model reports F1-scores of 0.9596, and 0.9612 using Bi-GRU and BiLSTM, respectively. Based on the results the model can successfully detect the type and index mutations in the DNA sequence more accurately and faster without the need for other supporting data and tools, and does not require re-alignment to reference sequences. This will greatly facilitate the user in detecting type and index mutations faster by entering only the DNA sequence. | Paper Link |
| YULI SUN HARIYANI | Connectivity & Convergence Instruments for Smart Living | Deep Learning Based Heart Rate Estimation Using Smart Shoes Sensor | Although heart rate is an important biomarker of the physical condition at active states of users, it is still difficult to be measured due to an ambient noise and movements. There have been several approaches proposed to obtain stable measurements in an active condition. However, these methods still need direct contact to users, and thus additional equipment to keep the contact are requested, resulting in inconvenience of its usage. This paper proposes a method to estimate the heart rate of the user using activity information from smart shoes sensors, which is relatively easy and robust to be recorded. For the accurate estimation of the heart rate, a new design of deep neural networks is proposed. The architecture extracts features of time-sequential patterns of sensor data with implementing CNN and LSTM model together. The model was validated with a `Leave-OneOut Cross-Validation method'. The results of the experiments are 10.21 ± 3.31 RMSE, 8.31 ± 2.81 MAE and 0.91 ± 0.09 correlation coefficient (Pearson) for the estimation of heart rate from smart shoes sensor data. | Paper Link |
| YULI SUN HARIYANI, HEESANG EOM, SUWHAN BAEK, CHEOLSOO PARK, JONGRYUN ROH, SAYUP KIM, SUKHOO LEE | Connectivity & Convergence Instruments for Smart Living | Deep Learning-Based Optimal Smart Shoes Sensor Selection For Energy Expenditure And Heart Rate Estimation | Wearable technologies are known to improve our quality of life. Among the various wearable devices, shoes are non-intrusive, lightweight, and can be used for outdoor activities. In this study, we estimated the energy consumption and heart rate in an environment (i.e., running on a treadmill) using smart shoes equipped with triaxial acceleration, triaxial gyroscope, and four-point pressure sensors. The proposed model uses the latest deep learning architecture which does not require any separate preprocessing. Moreover, it is possible to select the optimal sensor using a channel-wise attention mechanism to weigh the sensors depending on their contributions to the estimation of energy expenditure (EE) and heart rate (HR). The performance of the proposed model was evaluated using the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Moreover, the RMSE was 1.05 ± 0.15, MAE 0.83 ± 0.12 and R2 0.922 ± 0.005 in EE estimation. On the other hand, and RMSE was 7.87 ± 1.12, MAE 6.21 ± 0.86, and R2 0.897 ± 0.017 in HR estimation. In both estimations, the most effective sensor was the z axis of the accelerometer and gyroscope sensors. Through these results, it is demonstrated that the proposed model could contribute to the improvement of the performance of both EE and HR estimations by effectively selecting the optimal sensors during the active movements of participants. | Paper Link |
| ADI PRANESTHI, BUDHI IRAWAN, CASI SETIANINGSIH | Connectivity & Convergence Instruments for Smart Living | Earthquake Early Warning System Prototype Based Internet of Things and Time Pattern Analysis Using Backpropagation Algorithm | Earthquakes are vibrations that occur on the earth's surface due to the sudden release of energy from the inside that creates seismic waves. An earthquake is caused by the movement of the earth's crust (the earth's plate). The frequency of a region refers to the type and size of earthquakes experienced during a period. Along with the development of early earthquake detection system technology provides a solution to minimize earthquake events. This research will discuss the system's design to determine the occurrence of earthquakes through time pattern analysis and Peak Ground Acceleration value. By using the Radial Basis Function Method, which later to minimize the loss of life from earthquakes. And help the main tools owned by the government. This study aims to determine the occurrence of earthquakes from Peak Ground Acceleration values and time analysis patterns, which are obtained from the decision of the Backpropagation method with an accuracy rate of 88%. | Paper Link |
| ADI PRANESTHI, CASI SETIANINGSIH | Connectivity & Convergence Instruments for Smart Living | Earthquake Early Warning System Prototype Based Internet of Things and Time Pattern Analysis Using Backpropagation Algorithm | Earthquakes are vibrations that occur on the earth's surface due to the sudden release of energy from the inside that creates seismic waves. An earthquake is caused by the movement of the earth's crust (the earth's plate). The frequency of a region refers to the type and size of earthquakes experienced during a period. Along with the development of early earthquake detection system technology provides a solution to minimize earthquake events. This research will discuss the system's design to determine the occurrence of earthquakes through time pattern analysis and Peak Ground Acceleration value. By using the Radial Basis Function Method, which later to minimize the loss of life from earthquakes. And help the main tools owned by the government. This study aims to determine the occurrence of earthquakes from Peak Ground Acceleration values and time analysis patterns, which are obtained from the decision of the Backpropagation method with an accuracy rate of 88%. | Paper Link |
| ALIFANDA PINKAN LUDICA, PUTU HARRY GUNAWAN, ANIQ ATIQI ROHMAWATI | Connectivity & Convergence Instruments for Smart Living | SIMULASI PERGERAKAN RUNTUHAN LONGSOR MENGGUNAKAN MODEL SAVAGE-HUTTER DENGAN FINITE VOLUME METHOD | The avalanche is simulated using the Savage-Hutter model with Finite Volume Method (FVM) as a numerical solution in one dimension. The scheme used in FVM is collocated-grid. The aim of this research is to observe the avalanche based on different sediment types on the incline bed with the same initial sediment height. These simulations produce the value of velocity and height avalanche. For each type of sediment has a difference in velocity and height of avalanche affected by the internal angle of friction and the bed friction angle. Sediments with the highest bed friction angle have highest speed. The average velocity of each sediment are Quartz with u = 10.627, Yellow Sand with u = 7.437, and Rice with u = 2.1178 at time t = 1. | Paper Link |
| ANDRY PUTRA FAJAR, ANDRY PUTRA FAJAR, TITO WALUYO PURBOYO | Connectivity & Convergence Instruments for Smart Living | Experimental Study of Flood Type Distributed Denial-of-Service Attack in Software Defined Networking (SDN) Based on Flow Behaviors | Distributed Denial of Services (DDoS) attacks is one of well-known and dangerous threats to the current network which always exists and evolves in line with the development of the network itself. Current network development has entered the Software Defined Networking (SDN) era which offers centralized control and programmability network by decoupling the network control and data plane that bring on us a dynamic, cost-effective, manageable and agile platform. On the down-side, this centralized platform can bring new security challenges such as DDoS attacks on the central controller which could compromise the entire network. The most common DDoS attack is Flood based DDoS attack. This attack is quite easy to do and very effective strikes the target. This paper offers some experimental study for detecting this kind of DDoS attack using flow behaviors to give an idea for researcher about the DDoS attack and the effect for the network. | Paper Link |
| ASTRI NOVIANTY, CARMADI MACHBUB, SRI WIDIYANTORO, IRWAN MEILANO, DARYONO | Connectivity & Convergence Instruments for Smart Living | Tsunami Potential Prediction using Seismic Features and Artificial Neural Network for Tsunami Early Warning System | Tsunamis are categorized as geophysical disasters because tectonic earthquakes triggered most of their occurrences. The high number of deaths due to tsunami catastrophe has made many countries develop a tsunami early warning system (TEWS), especially countries prone to tectonic earthquakes. One of the crucial subsystems in a TEWS is the tsunami potential prediction subsystem. To provide an early warning of tsunami, the prediction must be carried out based on early observation of the seismic event before the tsunami. In this short time of computation, the calculation of seismic parameters can only produce some roughly estimated features. Hence, a proper inference method that can decide accurate predictions upon the features is urgently needed for the TEWS. Some existing TEWSs are using rule-based inference to decide the prediction and often overestimate the prediction of tsunami potential. This study tries to develop a tsunami-potential prediction system using the machine learning approach as its inference method. Seismic features extracted from P-wave seismic signals are used as input data for learning and classification using a backpropagation artificial neural network (ANN). The accuracy result is then validated by K-fold cross-validation. Our simulation results show that the utilization of backpropagation ANN has given better accuracy in tsunami prediction compared to one of the existing TEWS that does not use machine learning for its prediction. At least for some seismic events that occurred during 2010-2017, the proposed system results in fewer overestimated predictions than the existing TEWS referred. | Paper Link |
| AUDI CIPTA BAKTI, DIDIT ADYTIA | Connectivity & Convergence Instruments for Smart Living | Inversion of Tsunami by using Artificial Neural Network, study case: The 2018 Palu???s Tsunami | The earthquake that occurred in Palu City in September 2018 caused an avalanche of marine sediments in Palu Bay that leads to a tsunami generation which impacted the Palu City and its surrounding areas. Nevertheless, there is no information regarding the location, precise shape, and mechanism of the landslide event that generated the tsunami in Palu Bay. In this study, the initial condition of water elevation which is generated by the landslide in Palu Bay is estimated by using a machine learning approach, i.e. Artificial Neural Network (ANN). To apply this approach, sets of training data are needed for the ANN model. Here, we use numerical wave simulations with various initial conditions that are performed to build the training data. We use the SWASH model as the wave model to perform numerical simulation. The obtained training data are then used for tsunami inversion by using ANN. Although there is only one measured water elevation in Palu Bay during the 2018 tsunami, i.e. in Pantoloan port, in this paper, we use four signals at different locations that are used as input for the ANN inversion model, in order to estimate the initial shape and location of the tsunami. We observe that by using four points of observation for tsunami inversion give best result compared to results by using 1 to 3 observation points. Using four points, we obtain R 2 score of 0.98347 and RMSE score of 0.115345. | Paper Link |
| AWAL RAIS SANUBARI, PURBA DARU KUSUMA, CASI SETIANINGSIH | Connectivity & Convergence Instruments for Smart Living | Flood Modelling and Prediction Using Artificial Neural Network | Flood is one of the common types of natural disaster in Indonesia, we need a system that can predict the arrival of the flood is important for the Indonesian people, especially people who live a certain area of the river flow. Some parameters that can be used to predict the flood are water level and rainfall around the river. Modeling system to predict the flood must have the prediction results as accurate as possible in order to produce a good system in predicting floods. Therefore, in this study proposed method of artificial neural network to analyze flood prediction ability by using artificial neural network In this study case using artificial neural network Radial Basis Function. Radial Basis Function is a model of artificial neural network architecture consisting of three layers of which are the input layer, hidden layer, and output layer. The data used for the training and testing process are data of water level and rainfall data in 2015 in Dayeuhkolot. Prediction results in the training and testing process resulted in MAPE values are 0.047% and 1.05% for water level data and 4.97% and 29.1% for rainfall data with combination of hidden node = 35, learning rate = 0.2 and Spread constant = 1.1 with the target epoch maximum termination of 5000 epoch. | Paper Link |
| BUDI SYIHABUDDIN, BUDI SYIHABUDDIN, SOFIA FAUZIANA PUTRI | Connectivity & Convergence Instruments for Smart Living | Development of High Power Class-E Amplifier for Radio Communication of Tsunami Early Warning System | Utilization of radio communication is one of alternative methods which can be offered to the society in responding tsunami warning. In order to have large coverage, the used radio communication requires high power output of radio frequency (RF) amplifier. This paper deals with the development of high power class-E amplifier implemented for radio communication of tsunami early warning systems (TEWS). The proposed amplifier is developed using a power laterally-diffused metal-oxide semiconductor (LDMOS) transistor of BLF188XR type from Am-pleon as the main component. The frequency range of developed amplifier is 90MHz to 110 MHz which is suited for proposed radio communication of TEWS. Some attempts to attain high power amplification are performed through a circuit & EM simulator software. Based on the optimum performance, the proposed amplifier is then deployed on a printed circuit board (PCB) for experimental measurement. The result shows that the realized amplifier has the gain achievement more than 21 dB at the frequency of 100 MHz suitable for the desired application. | Paper Link |
| DAMMAR ADI SUJIANSYAH, KHOIRUL ANWAR, ALOYSIUS ADYA PRAMUDITA | Connectivity & Convergence Instruments for Smart Living | Biconical Antenna for Mobile Base Station for Post Disaster Area Wireless Communications | This paper proposes an antenna having a frequency ranges of 2G, 3G, 4G and 5G for disaster recovery wireless communications system. The antenna is also functioning for mobile base station (MBS) for both post-disaster and rural areas communications. In Indonesia, 2G-5G mobile communications frequency ranges are operated at 0.8-33 GHz. To cover areas with a radius of 5 km, we propose an Asymmetric Biconical antenna tested by a series of computer simulations, where the material of antenna is brass with a dimension of 261.96 mm × 304.59 mm × 304.59 mm, which is light suitable for MBS. We obtain return loss RL <; -10 dB with omni-directional radiation pattern and gain G > 1 dB. We expect that the proposed Asymmetric Biconical antenna contributes to the development of post-disaster area communications as well as contributions for disaster mitigation. | Paper Link |
| DEDE TARWIDI, DEDE TARWIDI, DIDIT ADYTIA, SRI REDJEKI PUDJAPRASETYA | Connectivity & Convergence Instruments for Smart Living | A reduced two-layer non-hydrostatic model for submarine landslide-generated tsunamis | The goal of this research is to develop an efficient numerical scheme for simulating the appearance of surface waves induced by submarine landslides. In this study, a reduced two-layer non-hydrostatic model is extended to include a time-varying solid bottom. This reduced two-layer non-hydrostatic scheme is computationally efficient, comparable to a one-layer scheme, but has the accuracy of a two-layer system. The accuracy and flexibility of the reduced two-layer non-hydrostatic scheme (abbreviated as NH-2LR) used here are explained by studying the dispersion relation. As benchmark tests, the NH-2LR model is validated using a variety of tsunami simulations generated by landslides, including impulsive vertical bottom motions, mass sliding on an inclined plane, and horizontal landslide motion. The numerical findings are shown to be in good agreement with the experimental observations, implying that the proposed model is a feasible alternative for modeling landslide-generated tsunamis. It is also shown that the new scheme reduces computational effort while maintaining high accuracy. | Paper Link |
| DIDIT ADYTIA, DEDE TARWIDI, ARKAN PRIYA ANGGANA HADNA | Connectivity & Convergence Instruments for Smart Living | Momentum Conservative Scheme for Simulating Wave Runup and Underwater Landslide | This paper focuses on the numerical modelling and simulation of tsunami waves triggered by an underwater landslide. The equation of motion for water waves is represented by the Nonlinear Shallow Water Equations (NSWE). Meanwhile, the motion of underwater landslide is modeled by incorporating a term for bottom motion into the NSWE. The model is solved numerically by using a finite volume method with a momentum conservative staggered grid scheme that is proposed by Stelling & Duinmeijer 2003 [12]. Here, we modify the scheme for the implementation of bottom motion. The accuracy of the implementation for representing wave runup and rundown is shown by performing the runup of harmonic wave as proposed by Carrier & Greenspan 1958 [2], and also solitary wave runup of Synolakis, 1986 [14], for both breaking and non-breaking cases. For the underwater landslide, result of the simulation is compared with simulation using the Boundary Integral Equation Model (BIEM) that is performed by Lynett and Liu, 2002 [9]. | Paper Link |
| DIDIT ADYTIA, DIDIT ADYTIA | Connectivity & Convergence Instruments for Smart Living | Analysis of bay bathymetry elements on wave amplification: a case study of the tsunami in Palu Bay | This research was inspired by the deadly tsunami that struck Palu Bay, Sulawesi, Indonesia on September 28, 2018. High waves were observed not only at the bay’s head but also in several other locations along the bay. This high amplification is most likely influenced by Palu Bay’s bathymetry and the underwater sill in front of Palu City. The numerical simulations of the two-dimensional shallow water equations were used to investigate these aspects. The numerical scheme is validated first with analytical solutions of standing waves in a parabolic bay, as a model of Palu Bay. The results show a significant amplification at the bay’s head, as a result of the bay’s reduced cross-sectional area. Some amplifications were also observed along the channel sides, and the role of the underwater sill was also investigated. Next, we carried out simulations with the actual Palu Bay bathymetry and a hypothetical tsunami $N-$wave and/or Gaussian humps. The simulation result depicts extremely high waves recorded along the Palu Bay coastline, which is consistent with the field survey; additionally, the wave signal at Pantoloan corresponds reasonably well to the tide gauge data. These findings indicate that Palu Bay’s bathymetry plays an important role in local wave amplification. | Paper Link |
| DOAN PERDANA, RIYAN HADI PUTRA, ONGKO CAHYONO, GUSTOMMY BISONO | Connectivity & Convergence Instruments for Smart Living | Analysis of Soil Substance Measurement System Based on Internet of Things (IoT) with Star Topology Network | Agricultural production in Indonesia is inseparable from the use of inorganic fertilizers and is difficult to separate in the cultivation of plants. In the design of a tool to measure Nitrogen, Potassium and Phosphorus in strawberry plants with star network topology method, the hardware used is the ground element measurement sensor, sensor Soil Moisture, and NodeMCU as a microcontroller. The elemental measurement sensor for soil measures nitrogen, potassium, phosphorus on the ground, sensor Soil Moisture. NodeMCU functions as a microcontroller and sends soil nutrient measurement data to the real time database. The real time database used in this work is firebase and using star topology network. We conclude that proves a device that can be used to measure soil substance in strawberry plants. We analyze for delay, throughput, packet received and packet loss depends on the number of devices used and the operator used. | Paper Link |
| FAISAL BUDIMAN, ERWIN SUSANTO, HUSNENI MUKHTAR, DOAN PERDANA | Connectivity & Convergence Instruments for Smart Living | Sistem Pemantauan Tanah Longsor Berdasarkan Laju Adsorpsi Air Pada Tanah Menggunakan Sensor Kelembaban, Kemiringan dan Suhu | This study examines the application of a landslide disaster monitoring system based on soil activity information that utilizes humidity, temperature, and accelerometer sensors. An artificial highland was built as the research object, and the landslide process was triggered by supplying the system with continuous artificial rainfall. The soil activities were observed through its slope movement, temperature, and moisture content, utilizing an accelerometer, temperature, and humidity sensors both in dry and wet conditions. The system could well observe the soil activities, and the obtained data could be accessed in real-time and online mode on a website. The time delay in sending the data to the server was 2 seconds. Moreover, the characteristics of soil porosity and its relevance to soil saturation level due to water pressure were studied as well. Kinetic study showed that the water adsorption to soil followed the intraparticle diffusion model with a coefficient of determination R2 0.99043. The system prototype should be used to build the information center of disaster mitigation, particularly in Indonesia. | Paper Link |
| FANNI HUSNUL HANIFA, WIDYA SASTIKA, SRI WIDANINGSIH, DENDI GUSNADI, EDWIN BAHARTA, RIZA TAUFIQ, DWI ANDI NUMANTRIS, ASEP MULYANA, UNANG SUNARYA | Connectivity & Convergence Instruments for Smart Living | PENINGKATAN KETERAMPILAN SISWA/I SMK YAYASAN ISLAM KOTA TASIKMALAYA MELALUI PELATIHAN DIGITAL MARKETING, PELATIHAN PEMBUATAN JELLY ART DAN PELATIHAN PENERAPAN TEKNOLOGI IOT DALAM SISTEM KEAMANAN DI SEPEDA MOTOR | SMK (Sekolah Menengah Kejuruan) merupakan salah satu Lembaga Pendidikan di Indonesia yang sederajat dengan SMA (Sekolah Menengah Atas), berbeda dengan SMA yang merupakan jenjang yang memang dipersiapkan untuk melanjutkan ke Universitas, SMK lebih mempersiapkan siswa-siswanya untuk siap bekerja setelah lulus sekolah. SMK Yayasan Islam merupakan salah satu dari 51 SMK yang ada di Kota Tasikmalaya. SMK Yayasan Islam berada di Jl. L. K.H. Mamun Sodik No. 50 Bojongkaum, Panglayungan, Kec. Cipedes, Kota Tasikmalaya, Jawa Barat. SMK Yayasan Islam merupakan SMK swasta yang memiliki 6 jurusan yaitu tata boga, tata busana, bisnis daring dan pemasaran, otomatisasi dan tata kelola perkantoran, teknik komputer dan jaringan dan teknik dan bisnis sepeda motor. Selama masa pandemi, kegiatan belajar mengajar di SMK Yayasan Islam dirasa kurang maksimal, hal tersebut tersebut terjadi karena bentuk kegiatan belajar mengajar yang seharusnya lebih banyak praktek menjadi sulit untuk dilaksanakan. Siswa/i SMK Yayasan Islam mayoritas berasal dari masyarakat sekitar dengan tingkat perekonomian menengah ke bawah, sehingga hanya kurang dari 10% siswa/i nya yang melanjutkan pendidikan ke jenjang yang lebih tinggi. Tujuan dari kegiatan pengabdian masyarakat ini adalah meningkatkan keterampilan siswa/i sesuai dengan masing-masing jurusan agar mampu bersaing di dunia kerja. Kegiatan pengabdian masyarakat yang dilaksanakan sudah sesuai dengan kompetensi dari seluruh dosen yang terlibat dalam kegiatan pengabdian masyarakat. Kegiatan abdimas meliputi pelatihan digital marketing bagi siswa/i jurusan bisnis daring dan pemasaran, pelatihan pembuatan jelly art bagi siswa/i jurusan tata boga dan pelatihan penerapan teknologi IOT dalam sistem keamanan di sepeda motor. Kegiatan pengabdian masyarakat dilaksanakan pada 2 Maret 2022, bertempat di ruang kelas SMK Yayasan Islam Kota Tasikmalaya dan diikuti oleh 79 orang siswa/i dari 3 jurusan. Hasil feedback dari masyarakat sasar diperoleh nilai dari jawaban setuju dan sangat setuju berada pada rentang sangat baik yaitu sebesar 98,73% didapatkan dari jumlah prosentase setuju dan sangat setuju. Rentang ini berada pada kategori penilaian “Sangat Baik”. | Paper Link |
| FRANSISCA MARGARET PASALBESSY, KHOIRUL ANWAR | Connectivity & Convergence Instruments for Smart Living | Analysis of Internet of Things (IoT) Networks Using Extrinsic Information Transfer (EXIT) Chart | The Internet-of-Things (IoT) is estimated to be deployed to serve billions of devices constructing super-dense networks, of which the performances are depending on the multiuser detection (MUD) capabilities to support more devices. This paper analyzes the decoding behaviour of Narrowband IoT (NB-IoT) and Single Carrier IoT (SC-IoT) networks using extrinsic information transfer (EXIT) chart to observe their throughput performances in low and high volume traffics. NB-IoT uses slotted ALOHA as its multiple access technique that discards collided packets, while SC-IoT uses coded random access (CRA) scheme, where the collided packets are to be resolved using successive interference cancellation technique, which is equivalent to peeling decoding at packet level. We also analyze network performances in terms of packet-loss-rate (PLR) and throughput using a series of computer simulations. Our results confirmed that SC-IoT using CRA has better performance than NB-IoT in terms of PLR, throughput, and gap of EXIT chart indicating that SC-IoT based on CRA scheme is a promising scheme for future IoT to serve massive number of users or devices. | Paper Link |
| GRISNA ANGGADWITA, DINI TURIPANAM ALAMANDA, ANGGRAENI PERMATASARI, IDZHAR INZAGHI SETIAWAN | Connectivity & Convergence Instruments for Smart Living | Mapping Communication Priority of Local Government Leaders through Instagram Captions in Publicising Smart City Programmes | Currently, social media is required to inform the public about local government programmes related to smart city. Advances in technology require the government to provide fast, precise and accurate information services. Local government leaders' communication patterns are crucial factors in the successful implementation of the smart city technology. This study aims to map the communication priorities of DKI Jakarta government leaders in publicising the smart city programmes using Instagram. The study, using a qualitative method with a content analysis approach, employs a case study involving an Instagram account owned by Anies Baswedan as the head of the DKI Jakarta regional government, analysing captions posted between February 2019 and February 2020. The ATLAS.Ti program was used for data processing. The study results reveal that the highest priority area for communication regarding the smart city programmes was Smart Economy. Moderate priorities included Smart Living, Smart People, Smart Governance and Smart Mobility. Lastly, Smart Environment had a low priority. This study demonstrates that Instagram can be an effective medium for building interaction between leaders and the community in delivering smart city programmes with balanced communication intensity for each programme. Thus, this study's findings are expected to provide a reference for local government leaders in balancing communication priorities for all smart city programmes. | Paper Link |
| HASANAH PUTRI, ATIK NOVIANTI, DADAN NUR RAMADAN | Connectivity & Convergence Instruments for Smart Living | Water Turbidity Alert System For IoT-Based Water Tank | Water has enormous benefits in daily life; quality and quantity maintained are required to be always ensured. In Indonesia, Bandung, many people keep clean water supplies in water tanks. The habit appeared as the result of their dependency on groundwater. However, some people also have already been connected to the government's water pipeline network, but they still need to have clean water tanks for prevention. The tanks need regular maintenances to ensure the stored water remains clean. When the tanks are rarely cleaned, moss appears, clogging the water pipe. The manual process of monitoring water conditions has several weaknesses, such as needing expert staff, taking more extended time, having a greater possibility of errors, and not presenting and neatly storing documentation. This paper aims to implement a water turbidity warning system in tanks and an IoT-based scheduling system for cleaning water tanks. The system will be implemented at several points of the house or water tank and can transmit data in real-time either alternately or simultaneously. The sensor detects water turbidity, and the microprocessor control unit node is in charge of processing the sensor reading data to calibrate and classify the turbidity level. Water turbidity values and levels are sent to Firebase, which resides in the cloud. The information from Firebase is passed and displayed on the application. The turbidity level of water was categorized into three: clean, turbid, and dirty. Managers, as the service providers, and consumers can monitor the tank's condition with a warning in the form of an indicator when the tank must be cleaned. | Paper Link |
| HENDI ANWAR, HENDI ANWAR | Connectivity & Convergence Instruments for Smart Living | KAJIAN PERANCANGAN KAWASAN PERUMAHAN PADA LOKASI RAWAN BANJIR DENGAN PENDEKATAN WATER SENSITIVE URBAN DESIGN (WSUD) DENGAN STUDI KASUS KAWASAN GEDEBAGE BANDUNG | Perkembangan laju pertumbuhan kota Bandung pada saat ini mengalami peningkatan yang sangat tinggi, dimana pertumbuhan sektor–sektor ekonomi yang dibarengi dengan tingkat pertumbuhan populasi manusia pada kota Bandung semakin besar. Hal tersebut mengakibatkan kebutuhan terhadap bangunan semakin meningkat, dengan adanya pembukaan lahan–lahan baru yang kemudian dibangun menjadi bangunan–bangunan pendukung segala aktifitas dalam kota Bandung salah satunya adalah memiliki fungsi perumahan. Namun maraknya pembukaan lahan baru mengakibatkan ruang–ruang hijau didalam kota yang memiliki fungsi sebagai daerah resapan air semakin berkurang yang otomatis memiliki dampak besar dalam potensi banjir akibat luapan air yang tidak terserap pada masing–masing area tersebut. Pendekatan Water Sensitive Urban Design (WSUD) merupakan suatu pendekatan rancang kota dan ruang hijau yang digunakan dalam perencanaan kawasan dengan sensitifitas yang tinggi terhadap air. Pendekatan WSUD ini diharapkan dapat menangani terhadap masalah kawasan yang memiliki potensi banjir | Paper Link |
| HUSNENI MUKHTAR, DOAN PERDANA, PARMAN SUKARNO, ASEP MULYANA | Connectivity & Convergence Instruments for Smart Living | Sistem Pemantauan Kapasitas Sampah berbasis IoT (SiKaSiT) untuk Pencegahan Banjir di Wilayah Sungai Citarum Bojongsoang Kabupaten Bandung | The needs of flood disaster management encourage various efforts from all scientific disciplines of science, technology, and society. This article discusses the efforts to prevent flooding due to the habit of disposing of their waste into rivers through an innovative waste management system using the approach and application of Internet-based technology (IoT). Previous research has produced a prototype of the waste level monitoring system. In this research, the prototype was developed into a practical technology, called SiKaSiT (IoT Based Trash Capacity Monitoring System). This technology aims to assist janitor in monitoring, controlling and obtaining information about trash capacity and disposal time easily through an application on the smartphone in real-time and online. The system was made using a level detection sensor integrated with NodeMCU and Wi-Fi, MQTTbroker-protocol and Android-based application. Furthermore, the system was implemented in Bojongsoang adjacent to the Citarum river, where the water often overflowed due to the high rainfall and volume of trash around it. The results of system testing in the field shown good performance with value ranges of reliability is (99,785- 99,944)% and availability is (99,786- 99,945)%. SiKaSiT has several advantages over other similar systems. First, there is an application on the user's smartphone to monitor the capacity of trash and notification for full-bin. Second, the ability to operate on a small-bandwidth internet network because the throughput time is only around 0.59 kbps, thereby saving internet bandwidth consumption. This system has also helped overcome the problem of community trash management in Kampung Cijagra, where 60% of them gave feedback "agree" and the rest "strongly agree". | Paper Link |
| HUSNENI MUKHTAR, DOAN PERDANA, PARMAN SUKARNO, ASEP MULYANA | Connectivity & Convergence Instruments for Smart Living | Sistem Pemantauan Kapasitas Sampah berbasis IoT (SiKaSiT) untuk Pencegahan Banjir di Wilayah Sungai Citarum Bojongsoang Kabupaten Bandung | The needs of flood disaster management encourage various efforts from all scientific disciplines of science, technology, and society. This article discusses the efforts to prevent flooding due to the habit of disposing of their waste into rivers through an innovative waste management system using the approach and application of Internet-based technology (IoT). Previous research has produced a prototype of the waste level monitoring system. In this research, the prototype was developed into a practical technology, called SiKaSiT (IoT Based Trash Capacity Monitoring System). This technology aims to assist janitor in monitoring, controlling and obtaining information about trash capacity and disposal time easily through an application on the smartphone in real-time and online. The system was made using a level detection sensor integrated with NodeMCU and Wi-Fi, MQTTbroker-protocol and Android-based application. Furthermore, the system was implemented in Bojongsoang adjacent to the Citarum river, where the water often overflowed due to the high rainfall and volume of trash around it. The results of system testing in the field shown good performance with value ranges of reliability is (99,785- 99,944)% and availability is (99,786- 99,945)%. SiKaSiT has several advantages over other similar systems. First, there is an application on the user's smartphone to monitor the capacity of trash and notification for full-bin. Second, the ability to operate on a small-bandwidth internet network because the throughput time is only around 0.59 kbps, thereby saving internet bandwidth consumption. This system has also helped overcome the problem of community trash management in Kampung Cijagra, where 60% of them gave feedback "agree" and the rest "strongly agree". | Paper Link |
| INDRARINI DYAH IRAWATI, AKHMAD ALFARUQ, SUGONDO HADIYOSO, DADAN NUR RAMADAN | Connectivity & Convergence Instruments for Smart Living | IoT-based Distributed Body Temperature Detection and Monitoring System for the Implementation of onsite Learning at Schools | During the Covid-19 pandemic, teaching and learning activities were carried out virtually. It has been running for more than one year. When the trend of Covid-19 cases decreased, onsite learning began to be trialed by implementing strict health protocols. One of the important parameters for the first screening is body temperature because 99% of Covid-19 patients have fever. Therefore, a student temperature measurement mechanism is needed before entering the school area. A number of temperature detectors should be located to prevent queues. A distributed real-time monitoring system as well as data records are required for daily evaluations. Therefore, in this study, a distributed system for measuring body temperature was designed and implemented with data recording. This system runs online real-time on an internet network client server application. This system consists of four temperature detectors connected to a mini-computer as data control and an access point to a dedicated network. All sensor nodes can send data simultaneously. A web server application is provided for data storage and access to the client. From testing the proposed system, it is known that the system can send real-time data with a delay of <150 ms on several measurements and other measurements >150 ms because it really depends on the quality of internet service. The application can run an alarm function if it finds a temperature exceeding the threshold. This system has been implemented in one of a private school in the city of Bandung. With this system, it is hoped that it can support onsite learning activities in schools. | Paper Link |
| KHOIRUN NI’AMAH, I NYOMAN APRAZ RAMATRYANA, KHOIRUL ANWAR | Connectivity & Convergence Instruments for Smart Living | Coded Random Access Prioritizing Human Over Machines for Future IoT Networks | In this paper, we propose a new access technique to prioritize human over machines for future wireless Internet-of-Things (IoT) networks. This paper develops coded random access (CRA) using repetition codes as a multiple access scheme for ultra high dense networks, which is expected to serve millions of nodes, covering human and machines simultaneously. Repetition codes are selected because of its simplicity in design and implementation, where a packet is copied and transmitted to the common destination according to the designed rate. This paper aims to maximize the number of users (both human and machines), where the priority of access is given to the human. In this study, human and machines are using coding and modulation system that match each other, according to extrinsic information transfer (EXIT) chart, to minimize loss. We evaluate the performance of the proposed access scheme using simple degree distribution for human and machines groups in terms of packet-loss-rate and throughput. We found that human group can be prioritized indicated by low packet-loss rate (PLR) and higher throughput. These results are believed to be useful for future application of super dense networks involving devices in the Internet-of-Things (IoT) networks. | Paper Link |
| LUTHFI FAUZI | Connectivity & Convergence Instruments for Smart Living | Experiment of Routing for Mobile Cognitive Radio Base Station (MCRBS) | Mobile Cognitive Radio Base Station (MCRBS) is an alternative technology for the recovery of telecommunications networks after a natural disaster happens, where routing is then playing an important role. This paper makes experiments for MCRBS routing technology using three universal software radio peripheral (USRP) devices of X310, B200 mini-i, and B210 and uses GNU Radio software. MCRBS has a routing ability to find the best and stable route in carrying information from victims of the disaster area network to the normal networks. One of the best route indicator is the high level of receive signal-to-noise ratio (SNR) to provide small error. The types of routes in MCRBS routing experimented in this paper are (i) direct and (ii) relay routes, where the route is evaluated in terms of bit-error rate (BER) and frame-error rate (FER) performances before being used by the MCRBS. The results show that the relay device at a closer distance to both the transmitter and receiver has better BER and FER performances. This paper found that in some distances, the relay is better used by the MCRBS, otherwise the direct should be selected by the MCRBS. The results of this paper is expected to provide contributions to the development of technology for disaster recovery networks. | Paper Link |
| MAULANA REZI RAMADHANA, ASSAS PUTRA, TWIN AGUS PRAMONOJATI, RIZCA HAQQU, PRADIPTA DIRGANTARA | Connectivity & Convergence Instruments for Smart Living | Learning Readiness as a Predictor of Academic Resilience in Online Learning during School from Home | Learning readiness is considered as a supporting factor in , academic resilience. Since the situation of school closure and learning from home due to the COVID19 pandemics, there have been changes in learning methods that require students to readily use online learning. Unfortunately, students' readiness in online learning has not been widely discussed in terms of its effect on the student’s academic resilience. The purpose of this study was to provide information on whether there was a significant relationship between online learning readiness and students' academi c resilience during the schoolfromhome period. Participants in this study consisted of 1.681 students from five high schools in Bandung, Indonesia. The research used questionnaires that were based on the online learning readiness scale and the academic r esilience scale. The questionnaires were distributed online. The data in this study were then analyzed using correlational and regression methods. The results showed that there was a moderately significant relationship between student readiness in online l earning and student academic resilience during the school-- fromhome period. Also, online learning readiness significantly predicted student academic resilience through the dimensions of motivation for learning and self directed learning. This study emphasi zed the importance of student readiness in online learning as a new learning strategy during school from home in increasing academic resilience and success. | Paper Link |
| META KALLISTA, IG. PRASETYA DWI WIBAWA | Connectivity & Convergence Instruments for Smart Living | Neural Network on Tsunami Waves Prediction Detector Tools Using Tectonic Earthquakes Data | On 26 December 2004, tsunami waves were generated by undersea megathrust earthquakes particularly hit the Banda Aceh-Indonesia, also Thailand, Sri Lanka, India. The effect of tsunami waves can be very damaging to the coastal areas even more to the land around the coast. It is very interesting to study the relation between the magnitude of the undersea earthquakes and the tsunami. Therefore, we construct an early warning system using Neural Network to predict the tsunami using data from Indonesian Meteorology, Climatology, and Geophysical Agency that integrated with a hardware tool. The hardware tools will show the prediction result and send a short message. | Paper Link |
| MEUTIA GINA SALSABILA; MUHAMMAD ARY MURTI; AZAM ZAMHURI FUADI | Connectivity & Convergence Instruments for Smart Living | Design Of 3 Phase Kwh Meter Communication Based On Internet of Things (IoT) Using LoRa | kWh meter is a tool to measure the use of electrical energy. This tool is widely used at home and in industry. Most kWh meters can only display the amount of electricity used from the display on the kWh meter. This causes power users to be unable to view or monitor electricity usage remotely. This Internet of Things (IoT) based kWh meter communication design allows all data from the kWh meter to be sent to the gateway and forwarded to the IoT cloud. LoRa (Long Range) communication will be used in this research. The kWh meter that has been added with IoT technology is expected to make it easier for users to monitor the electricity consumption data from anywhere. The results of the tests in this final project, the device is able to read the data on the amount of electricity from the kWh meter. The LoRa communication module can send the data taken from the kWh meter to the gateway to be displayed in Antares. The data transmission results have an average SNR 9.81 dB, RSSI −78.14 dBm, delay 3.546 seconds, and packet loss 1.11%. | Paper Link |
| MIA ROSMIATI, MOCHAMMAD FAHRU RIZAL, ILMAN REZZA DEWANTARA PERMANA | Connectivity & Convergence Instruments for Smart Living | Data Communication Using LoRa Module for Transmitting information of Flood | Floods are natural disasters that often occur due to overflowing rainwater in residential areas. This disaster is often caused by a blockage of water flow in the water channel. Flood disasters often occur in residential areas adjacent to rivers, such as the Bojong Soang area of Bandung, Indonesia, which every year experiences floods due to overflowing water from the Citarum River. The slow flood information received by the surrounding community from the local government has caused many moral and material losses felt by the surrounding community. Therefore we need a flood detection infrastructure that can provide information quickly to local residents. With the use of water level sensors that can detect water levels and water flow sensors that can detect the speed of water flow and be integrated with raspberry Pi as a data processor, the system can provide information regarding the flooding around the Citarum river. In addition, by using the LoRa module which does not require an internet connection that will be used as a medium of data communication from sensors to the data center, the condition of the Citarum River water can be easily monitored at any time. From the testing that has been done, the process of sending data from the sensor to a data center that has a distance of 400 meters has a 1-5 second delay | Paper Link |
| MOH AQSA ALMUBARAKS, RISMA NUR DAMAYANTI, LUTHFI RAMADANI | Connectivity & Convergence Instruments for Smart Living | Applying Action Design Research to Digital Social Innovation: A Case of Automated Flood Detection System in Rural Region | Digital Social Innovation (DSI) is an action-oriented initiative aiming to tackle social challenges by introducing digital technologies. However, the extant literature tends to focus on the technological output of innovation and less on the methodological domain, particularly regarding the in-depth engagement with the social context, which hinders the construction of emergent social knowledge. This study presents a showcase of how Action Design Research (ADR) can be a promising methodological endeavor to a digital social innovation project. Based on a DSI project on the development of an automated flood detection system in a rural village in Indonesia, we show how innovation can be enacted as a collaborative and reflective process between the innovators and the community, which further enables us to formalize an understanding of the social context in innovation. This study elaborates the methodological guidelines to DSI while also reveals the contextual differences inherent to bottom-level development of a nation, in which formal and informal social actors at the village level both take the same role in assisting national endeavor toward development. | Paper Link |
| MUDY SOLEHMAN, FAIRUZ AZMI, CASI SETIANINGSIH | Connectivity & Convergence Instruments for Smart Living | WEB-BASED FLOOD WARNING SYSTEM USING DECISION TREE METHOD | Flooding is a disaster that often occurs in Indonesia, especially in the Dayahkolout Region from the beginning until now, and causes very significant damage in life. Based on this problem, we need a system that is able to handle the problem. This system is designed using the Decision Tree C4.5 Algorithm method to predict potentially flooded areas based on parameters that will later be connected to the Internet of Things. The parameters used are water level, rainfall, and water discharge. Where each parameter will be connected to IoT and the results of the predictions will later be shown on the web application. From the results of testing that has been done, the C4.5 algorithm has the best performance on the 70%: 30% data partition which has an accuracy of 100%. | Paper Link |
| MUHAMMAD ADITYA TISNADINATA, NOVIAN ANGGIS SUWASTIKA, RAHMAT YASIRANDI | Connectivity & Convergence Instruments for Smart Living | Sistem Peringatan Dini Gempa Bumi Multi Node Sensor Berbasis Fuzzy Dan Komunikasi IoT | Earthquake detection can be measured by calculating the acceleration of movement or vibra-tion horizontally and vertically, the existence of P (Prime) waves and S (Secondary) waves areindicative of an earthquake. One of the factors that can be used in detecting and calculating thestrength of an earthquake is by calculating the acceleration of vibrations that occur horizontallyin the P wave. By utilizing it, it can be seen the magnitude of the earthquake that occurred andcan provide a warning as quickly as possible to the community. The technology of Internet ofThings(IoT) allows the system to be able to read data automatically and continuously withouthaving to involve many people. In conducting earthquake classifications this system uses thealgorithm Fuzzy Logic which has the characteristics of being able to process informationquickly with low complexity so that the system can provide a warning as quickly as possible.In system data validation uses several levels of validation consisting of device servers andmain servers with different functions and objectives. The results of this study in 1000 trials,the system can carry out the validation and classification process with the average speedproduced is 10-15 seconds for one data processing with the level of suitability of the finalresults on the scale of SIG BMKG around 81.8% average. | Paper Link |
| MUHAMMAD ADITYA TISNADINATA, NOVIAN ANGGIS SUWASTIKA, RAHMAT YASIRANDI | Connectivity & Convergence Instruments for Smart Living | Sistem Peringatan Dini Gempa Bumi Multi Node Sensor Berbasis Fuzzy Dan Komunikasi IoT | Deteksi gempa bumi dapat diukur dengan menghitung percepatan gerakan atau getaran secarahorizontal dan vertikal, adanya Gelombang P (Primer) dan Gelombang S (Sekunder) menjadiindikasi akan terjadinya gempa bumi. Salah satu faktor yang dapat digunakan dalam mende-teksi dan menghitung kekuatan gempa bumi adalah dengan menghitung percepatan getaranyang terjadi secara horizontal dalam Gelombang P. Dengan memanfaatkan hal itu dapatdiketahui besaran gempa yang terjadi dan dapat memberikan peringatan secepat mungkinpada masyarakat. TeknologiInternet of Thingsmemungkinkan sistem untuk dapat membacadata dengan secara otomatis dan terus menerus tanpa perlu banyak melibatkan manusia.Dalam melakukan klasifikasi gempa bumi sistem ini menggunakan algoritmaFuzzy Logicyang memiliki karakteristik dapat mengolah informasi dengan cepat dengan kompleksitasrendah sehingga sistem dapat memberikan peringatan secepat mungkin. Dalam validasi datasistem menggunakan beberapa tingkat validasi yang terdiri dari server perangkat dan serverutama. Hasil daripada penelitian ini dalam 1000 kali percobaan, sistem dapat melakukanproses validasi dan klasifikasi dengan rata-rata kecepatan yang dihasilkan adalah 10 - 15detik untuk satu kali pengolahan data dengan tingkat akurasi hasil akhir pada skala SIGBMKG dengan rata-rata 81.8%. | Paper Link |
| MUHAMMAD ALIF AKBAR, SATRIA MANDALA | Connectivity & Convergence Instruments for Smart Living | IoT on Heart Arrhythmia Real Time Monitoring | Heart monitoring is popular in the recent 5 yearns. We can see this with emergence of variouscardiovascular monitoring products based on wearable sensors. Those products commonlycommunicates using radio telemetry which has expensive operational costs. Some researchtry to implement internet of things (IoT) concept to solve the issue. However, those IoTimplementation aren’t efficient enough. The research are only focused on how to read thesensors data and allowing it to be monitored on real-time. This research proposed a cloudbased IoT architecture to monitor arrhythmia, one type of a common heart attack, which moreefficient than previous research. Arrhythmia detector that used in this paper is an improvementof algorithm proposed by Tsipouras et al, which using R peak on ECG. The system proposedon this paper has been tested using MIT-BIH datasets and has result 93.11% accuracy against3 arrhythmia class, that is PAC, PVC and VT. The interesting result is that by implementingIoT, the R-Peak detection algorithm’s execution time decreased up to 30% compared to hasbeen proposed by Pan and Tompkins. The average of execution time of every sample isdecreased to 0.00749 ms. | Paper Link |
| NABILLA PUTRI ARISKA, DHARU ARSENO | Connectivity & Convergence Instruments for Smart Living | ANALISIS EKSPERIMEN DETEKSI STRUKTUR RONGGA DI BAWAH PERMUKAAN TANAH DENGAN GPR | Population increase in Indonesia affects the needs for regional expansions and infrastructures development. Theinfrastructure development process needs to go through many stages, especially for areas where data was notproperly collected beforehand. There are many cases of planting PGN gas lines, PLN cable networks, and culvertsthat do not comply with the established standards. To prevent damages to the channel in the development process,it can be anticipated by identifying regional infrastructure. It is hoped that the identification process of regionalinfrastructures can help related parties to carry out development more effectively. The identification process canbe assisted using a Ground Penetrating Radar (GPR) which could detect objects below the ground surface.In this experiment, an experimental analysis will be carried out by modeling conditions that resemble thesituation in the field. Data collection will apply Ultra Wideband frequencies (UWB) that allows precise distancemeasurement and high-resolution imagery results. GPR will be modeled with a tool, namely Vector NetworkAnalyzer (VNA) which functions to transmit and process the transmitted signal.The A-scan method was used to detect channels with cavities of 5 cm and 15 cm. In the A-scan method, bothmodels can be detected according to the size of the channel being modeled. The B-scan method used can show theboundaries of the medium in a 2-dimensional image. | Paper Link |
| NI KOMANG EGA KARTIKA, MUHAMMAD ARY MURTI, CASI SETIANINGSIH | Connectivity & Convergence Instruments for Smart Living | Floods Prediction Using Radial Basis Function (RBF) Based on Internet of Things (IoT) | Massive and continuously rainfall will cause floods. Floods can cause people's activities in the area to be hampered. With the technology that grows rapidly, people can get information easily. This Final Project is made to give information about the result of floods prediction using a technology called Internet of Things (IoT). This floods prediction is using Radial Basis Function. The data will be received from Citarum River Hall. The Information that used from Citarum River Hall is rainfall and river water debit. The result from Radial Basis Function Neural Network will be sent to an android application that displays the opportunity of flooding. Using epoch as much as 700 giving error value of TMA equal to 0.027 and error value of CH equal to 0.002, a learning rate of 0.00007 giving error value of TMA equal to 0.286 and error value CH equal to 0.002, and a hidden neuron of 2 giving error value of TMA equal to 0.6483 and error value of CH equal to 15.999 can be used to predict the flooding. | Paper Link |
| NI KOMANG EGA KARTIKA; MUHAMMAD ARY MURTI; CASI SETIANINGSIH | Connectivity & Convergence Instruments for Smart Living | Floods Prediction Using Radial Basis Function (RBF) Based on Internet of Things (IoT) | Massive and continuously rainfall will cause floods. Floods can cause people's activities in the area to be hampered. With the technology that grows rapidly, people can get information easily. This Final Project is made to give information about the result of floods prediction using a technology called Internet of Things (IoT). This floods prediction is using Radial Basis Function. The data will be received from Citarum River Hall. The Information that used from Citarum River Hall is rainfall and river water debit. The result from Radial Basis Function Neural Network will be sent to an android application that displays the opportunity of flooding. Using epoch as much as 700 giving error value of TMA equal to 0.027 and error value of CH equal to 0.002, a learning rate of 0.00007 giving error value of TMA equal to 0.286 and error value CH equal to 0.002, and a hidden neuron of 2 giving error value of TMA equal to 0.6483 and error value of CH equal to 15.999 can be used to predict the flooding. | Paper Link |
| NOVIA NURHIDAYAH PRIHATININGTYAS, KHOIRUL ANWAR, ALOYSIUS ADYA PRAMUDITA | Connectivity & Convergence Instruments for Smart Living | Microstrip Rotman Lens for Mobile Base Station Backbone in Disaster Area Networks | Mobile Cognitive Radio Base Station (MCRBS) is a technology to recover network communication temporarily in disaster areas. The communications can be built by constructing the backbone network, where it is formed by connecting multiple MCRBSs with point-to-point radio communication link considering the best routing algorithm. The MCRBS antenna system should has an ability on producing the beam into specific direction to establish point-to-point radio communication link. However, there is no capable system to support the MCRBS antenna system for directing the beam. Therefore, it requires a system that can assist in focusing the beam of antenna system for the MCRBSs connectivity. In this paper, we propose Rotman Lens that has beamforming capability to support radio backbone communication for multiple MCRBSs connectivity in post-disaster networks. The Rotman Lens is chosen to generate amplitude and phase shift in performing beamforming technique with simultaneous signal transmissions from MCRBS to others without moving the antenna system. In this research, the Rotman Lens was evaluated through a series of computer simulations and laboratory experiments to investigate the lens performance. The final proposed lens provides the experiment results for all beam ports with an average value both return loss and mutual coupling of less than −15 dB and −20 dB, where the magnitude deviation in average value and the maximum error of phase difference are 1.172 dB and 6.014º , respectively. The proposed Rotman Lens is also confirmed to be capable of controlling beam with scanning capability of −27º , −14º , 0º , 14º , 27º to support wireless communication backbone link among MCRBSs in disaster recovery networks | Paper Link |
| NOVIAN ANGGIS SUWASTIKA; QORI QONITA; MUHAMMAD AL MAKKY; MASLIN MASROM; TAUFIK SLAMET | Connectivity & Convergence Instruments for Smart Living | IoT-Based Photography Practice Learning Design for Basic Photography Subjects at Indonesian Vocational High Schools | In the Industry 4.0 era, the Indonesian government implemented the “Merdeka Belajar” or Independent Learning curriculum to improve the competence and skills of vocational school graduates. This curriculum gives schools flexibility and autonomy to design learning tailored to students’ ability levels, relevant to the industry, and contextual. One technology pillar of Industry 4.0 widely implemented in various fields is the Internet of Things (IoT). This research proposes an architecture design for implementing IoT-based technology for basic photography learning in broadcast and film majors in Indonesian Vocational High Schools (VHS). The challenges in learning basic photography are limited tools, practice time, fast and accurate feedback to students, and data on all student activities. The proposed system is implemented in a photography laboratory. The proposed system can store student practice activities, gamification-based game leveling, system automation according to levels and tasks, and provide fast and accurate feedback. This research’s output is system design, consisting of system architecture design and Unified Modeling Language (UML) design. UML diagrams built in this study include use cases, activities, and entity-relationships diagrams. | Paper Link |
| NUNUN ABDURRAHMAN, DIDIT ADYTIA, ADIWIJAYA | Connectivity & Convergence Instruments for Smart Living | Supervised Artificial Neural Network approach for Tsunami Inversion: A Case Study from 2018 Gunung Anak Krakatau | The eruption of Gunung Anak Krakatau (GAK) in 2018 caused a flank failure which resulted in a tsunami that affecting the coast of Western Java and Southern Sumatra. However, the location and mechanism of the landslide are still unclear. In this study, the location of the avalanche point, and the initial shape of the tsunami will be predicted by using a tsunami inversion via sof t computing approach. The inversion utilized the measured signals from four buoy stations in the Sunda Strait. To that aim, a soft computing approach Artificial Neural Network (ANN) is used for the inversion. Training data for the ANN model are obtained by performing several scenarios of tsunami numerical simulations. The numerical simulations are simulated by using SWASH model. Ten shapes of initial conditions are simulated for every 2 hours of simulation with the aim to obtain signals at four buoy locations. These four measured signals are then used for the inversion. The result of inversion shows a promising result with the accuracy of R value of 0.96. | Paper Link |
| OTRINANDA GANDHI; MOHAMAD RAMDHANI; MUHAMMAD ARY MURTI; CASI SETIANINGSIH | Connectivity & Convergence Instruments for Smart Living | Water Flow Control System Based on Context Aware Algorithm and IoT for Hydroponic | Farming with using a hydroponic technique became a solution for future farming technique. Hydroponic uses water to provide nutrient and oxygen for the plant. Water must be distributed equally in all part of hydroponic pipes, so that every plants get same amount of nutrient. In order to control the water flow to be distributed equally, this research used servo valve controlled by microcontroller based on Internet of Things (IoT). Lastly, this research also observe the plant grows in the hydroponic that using this system. The result shows that the system can make the plant growth, like plant length and leave width will be more equal between growing tube. | Paper Link |
| PERDANA, DOAN; IMADUDIN, MUHAMMAD; BISONO, GUSTOMMY | Connectivity & Convergence Instruments for Smart Living | Performance Evaluation of Soil Substance Measurement System in Garlic Plant based on Internet of Things with Mesh Topology Network Scenario | The high consumption followed by low production makes the government have to import garlic to meet domestic needs every year. To help increase garlic yields, a system designed to facilitate the process of measuring Nitrogen (N), Phosphorus (P), Potassium (K) content on plantation land in realtime using NPK sensor and nodemcu as microcontrollers and peovide the connectivity of realtime information using mesh topology. This system is an Internet of Things (IoT) based network, where internet connectivity is used to exchange information with each other with the objects around it. The result of the design of this system is a device to measure each element of N, P, and K as well as fertility status based on NPK values that have been obtained. And, with the IoT feature and mesh topology built in this device, the measurement data and whether or not the device works can be monitored easily through an android application that has been made on a smartphone. The mesh topology that built in this device is using painlessmesh library where the network built on the system is a true ad-hoc network, meaning that no-planning, central controller, or router is required. Any system of 1 or more nodes will self-organize into fully functional mesh. We conclude that the accuracy of the measurement data compared to the NPK meter analog (Doctor Plant) is above 90%. Based on the durability test of the device and the system using Xiaomi's power bank of 5000mAh, the device and the system work well for 30 hours without any problems. Moreover, the accuracy of the data measured and uploaded to the database is no error with a 100% compatibility rate. | Paper Link |
| PUTRI FATIMAH, CASI SETIANINGSIH, BUDHI IRAWAN | Connectivity & Convergence Instruments for Smart Living | Design of Early Warning System for Landslide Using Fuzzy Method Based on Android | In Indonesia, landslides are one of the many natural disasters that often occur during the rainy season. Especially in mountainous areas, cliffs, hills, which cause many losses. Therefore, it is necessary to create a landslide Early Warning System. Slope, vibration, and excessive water content in the soil are the leading causes of landslides. To measure these parameters, an Internet of Things (IoT) based system is used that is connected to various sensors. In this study, the fuzzy value obtained from the measurement of the MPU6050 Accelerometer and Gyroscope sensor, also Soil Moisture sensor sent to the Antares server using LoRa. In research, Fuzzy algorithm is used to analyze the sensor detection results in the form of three final decision rules based on the knowledge of a landslide expert, namely Safe, Alert, and Watch out, which can be seen on an android device with 90% accuracy value and 10% error. | Paper Link |
| PUTU HARRY GUNAWAN, FADHIL LOBMA | Connectivity & Convergence Instruments for Smart Living | Trough OpenMP Platform for Reducing Computational Time Cost in Underwater Landslide Simulation on Inclined Bottom | Simulation of underwater landslide becomes important, since underwater landslide phenomena is very dangerous in real life. One of the enormous disasters caused by this phenomena can be a Tsunami. Computer simulation of underwater landslide can reduce cost of time and money from conventional simulation (using laboratory). However, to obtain high resolution of computer simulation, large discrete points should be computed. In this paper, the numerical simulation of underwater landslide using two-layers shallow water equations (SWE) and OpenMP platform is elaborated. Here, the finite volume method framework using upwinding dispersive correction hydrostatic reconstruction (UDCHR) scheme is used. The results of numerical simulation is in a good agreement with the numerical simulation using Nasa-Vof2d numerical scheme. In parallel performance, speedup and efficiency of this numerical simulation are observed 2.8 times and 76% respectively at t=0.8 s final time simulation. | Paper Link |
| RAHMA METRIKASARI, ANDI SULASIKIN, YUDHISTIRA NUGRAHA ALEX LUKMANTO SUHERMAN | Connectivity & Convergence Instruments for Smart Living | Mapping of Flood Prone Area in Jakarta using Fuzzy C-Means | The geographical condition of Jakarta, which has low ground contours and an area passed by 13 river flows, causes a high risk of flooding. Therefore, the Jakarta Provincial Government needs evidence-based policies to deal with potential floods to protect residents from the threat of flood disasters. Mapping flood-prone areas in Jakarta can be a reference to minimize the significant loss and harm due to flooding. However, at this moment, it is still challenging to find an appropriate clustering model for classifying and mapping flood risk in Jakarta. Therefore, this study utilized K-Means and Fuzzy C-Means to analyze the flood-prone of 42 sub-districts in Jakarta based on four variables: groundwater usage, number of flood reports, water level during the flood, and land subsidence. According to Pseudo-F analysis, four is the optimum number of clusters. The result shows that the Fuzzy C-Means is a better model than K-Means in grouping flood-prone sub-districts in Jakarta based on the Icdrate test. The mapping of the flood-prone area in Jakarta using Fuzzy C-Means shows that some clusters are nearby. Furthermore, it suggests that sub-districts with high and relatively high flood risk are mainly located in West Jakarta and South Jakarta. Fuzzy C-Means promising results in mapping flood-prone areas to support the Provincial Government of Jakarta in formulating development plans for flood mitigation in Jakarta. | Paper Link |
| RANI GUSTI ANGESTI, FIFIN NUR HANIFAH, AMELIA KURNIAWATI, AFRIN FAUZYA RIZANA | Connectivity & Convergence Instruments for Smart Living | Decision Support System for Determining Flood Evacuation Locations in Kabupaten Bandung using the Simple Additive Weighting (SAW) Method | West Java Province is one of the provinces with the most flood disasters, which is 863 times and the highest number of floods occurred in Kabupaten Bandung is 228 times. Because floods often occur in Kabupaten Bandung make BPBD (Badan Penanggulangan Bencana Daerah) must respond to this problem, including by choosing a quick evacuation location. Choosing an appropriate evacuation route is very helpful in reducing losses and especially saving people's lives. So we need a computer-based system that can help BPBD in determining which location is most suitable as an evacuation location in Kabupaten Bandung by using the SAW (Simple Additive Weighting) method. This method is a simple weighting method or additional weighting to solve problems in a decision support system. The results of this study are a rank of recommendations for evacuation locations in Kabupaten Bandung. To test the feasibility and suitability of the system, a BlackBox test and user acceptance test was carried out. | Paper Link |
| RIZAL DWI PRAYOGO, NURUL IKHSAN | Connectivity & Convergence Instruments for Smart Living | An Optimization Model of Vertical Evacuation Scenario on Tsunami Disaster Mitigation | This paper proposes an optimization model to determine the best scenario of tsunami vertical evacuation. The purpose of this study is to mitigate the number of victims in evacuation zones within the available time before tsunami waves reach the coast. The tsunami is wave propagation at high speed as an impact after the earthquakes occurred under the sea. The population in the coastal areas are at risk from tsunami impact. In this study, Padang City is chosen as a case study. In the evacuation plan, the refugees are evacuated to the vertical evacuation structures that have enough height to raise refugees higher than the level of tsunami tide. An optimization model is formulated with linear programming using Matlab, by the objective function is to minimize the evacuation time with the constraints of the estimated time of tsunami arrival, the capacity of each vertical evacuation structures, and the number of refugees. The simulation results show that all refugees are successfully evacuated according to the proportions within available evacuation time. The results of this study can be implemented to support the tsunami disaster mitigation by the Government in Padang City. | Paper Link |
| RIZAL DWI PRAYOGO, SITI AMATULLAH KARIMAH | Connectivity & Convergence Instruments for Smart Living | Numerical Simulation of Tsunami Evacuation Route Planning in Padang Using Linear Programming | This paper proposes an implementation of linear programming in tsunami horizontal evacuation routes decision making. A tsunami is a huge wave that travels with high speed towards a coastal area. Padang is the capital city of West Sumatera Province that is located in the coastal area as well as have a history of the tsunami disaster. The main problem is only a few minutes of the interval for the tsunami waves arrive at the coast after tsunami hazard warning. In the evacuation concept, the evacuees from the high-risk zone are moving to the shelter through a safe evacuation route. This research aims to determine the optimal evacuation routes with the constraints of a few minutes of the interval of evacuation time, the capacity of each shelter, and the number of evacuees. The evacuation time is an objective function that depends on the number of population, people density, road width, distance from the high-risk zone to the shelter, and velocity. The numerical simulation is done using linear programming with Matlab. The results show that all evacuees in each high-risk region are proportionally evacuated to available shelter areas within allocated evacuation time. The results of this research can be used for supporting tsunami evacuation plans and disaster resilience management by the authorities in Padang. | Paper Link |
| RIZKI ARDIANTO PRIRAMADHI, AULIA HAVIZ FAJRI, MUHAMMAD ARY MURTI | Connectivity & Convergence Instruments for Smart Living | DESIGN OF EARTHQUAKE EARLY WARNING SISTEM BASED OMRON D7S VIBRATE SENSOR | Indonesia is a country that is very prone to earthquake. This disaster can have a very large impact from main destruction of ground motion and secondary destruction from building rupture. By providing early warning system, secondary disasters caused by the earthquake, such as fire due to gas leakage, electric shock or loss of important personal data, can be prevented. This research designs an early warning system against earthquakes, where the sensor used was 3d accelerometer-based OMRON D7S vibration sensor. A warning is also sent to personnel or web-server via GSM communication. The system calibrated with a standard tool MEISEI G401 owned by Indonesian Earthquake Agency (BMKG). Test showed that early warning system have accuracy value of 74.32%. From the testing that has been done, the time delay in sending data is greatly influenced by the state of the surrounding environment. Data is sent and stored to an SQL-based database, with an average delivery delay of 1.7 seconds. This system successfully provides an early warning using the buzzer alarm as desired. The percentage of success of the buzzer turns on as desired is 100%. | Paper Link |
| SENO ADI PUTRA | Connectivity & Convergence Instruments for Smart Living | Real Time Bridge Dynamic Response: Bridge Condition Assessment and Early Warning System | Present study investigating the use of wireless sensor networks (WSNs) in the assessment of bridge condition as well as early warning system. The WSNs are used to measure the acceleration occurred on the bridge and the mode shape of the bridge as the excitation loads passing through the bridge. Fast Fourier Transform (FFT) is applied to transform the measured acceleration to get the frequency of the bridge dynamic response. Numerical integration is applied to determined the acceleration to get the displacement of the bridge dynamic response. Implementing structural dynamics equation, the effective stiffness of the bridge can be determined using the frequency. The effective stiffness and the bridge dynamic response are then used to obtained the bridge condition and load ratings. A scaled model of steel truss bridge and miniature truck with various loads were used to simulate the use of WSNs in bridge assessment, which were also used to validate the finite element model. The finite element model was then used to simulate various scenarios, including the scenarios in which the bridge elements had various level of damages. The behaviors of bridge with various level of damages can be used to identify the location and the level of damages in the bridge and were found to be useful as early warning system for bridges condition and load ratings. | Paper Link |
| SITI HAJAR KOMARIAH, ANANDA RISYA TRIANI, RD ROHMAT SAEDUDIN | Connectivity & Convergence Instruments for Smart Living | Analisa Komparasi Kelayakan Penyelenggaraan Layanan Sensor Gempa Dan Video On Demand Dengan Memanfaatkan Lisensi Frekuensi 3.3 Ghz Dan Bandwidth 12.5 Mhz | The license for 3.3 GHz frequency and 12.5 MHz Bandwidth (BW) has been implemented and given to several companies. One of the license holders is a telecommunications company located in Bandung and Jakarta. This 3.3 GHz frequency, in the map for the performance of 5G telecommunication services, includes candidates for the medium class working frequency which is predicted to be auctioned by the government to become the working frequency for 5G performances. Several companies have an interest in identifying the use of 3.3 GHz and 12.5 MHz BW licenses outside the stand-alone performance of 5G technology-based broadband telecommunications services. Initial identification and studies show that there are two services that have the potential to be deployed by utilizing the frequency range and BW, namely earthquake sensor network services and Video on Demand (VoD) in DKI Jakarta. To ensure the feasibility of performing these two services based on the technology owned by the telecommunications company, further studies are needed by analyzing them from the market aspect, technical aspect, and financial aspect. This research was conducted using a techno-economic approach and analysis. The novelty in this research is the implementation of new 5G technology licensed with 3.3 GHz frequency and 12.5 MHz bandwidth in the new case, namely the earthquake sensor network compared to other services, namely video on demand. The next process is the comparison of the performance feasibility parameters (NPV, IRR, and Payback Period) based on the results of a study of the two services that can be carried out to determine the priority service candidates to be provided by the telecommunications company. The results show that in terms of operational and financial aspects, the earthquake sensor project will be more profitable for the company because the effort spent is not too large. | Paper Link |
| SUSSI, SOFIA, NURWULAN, DEDE, RIKA, MUHAMMAD, AMRI | Connectivity & Convergence Instruments for Smart Living | Agrotech: Penyiraman Tanaman Dan Pemantauan Kadar Air Dalam Tanah Berbasis Internet Of Things | Watering is something important in agriculture. In general, watering is done manually where farmers directly water the plants with a hose. The direct watering method takes a long time, farmers have to visit the farm location and cannot be monitored remotely in real-time. The purpose of this research is to make watering plants based on the internet of things (IoT) using NodeMCU ES8266. Soil moisture data is monitored through the Argotech application in real-time. The soil moisture parameter will determine the automatic watering decision which indicates the water pump is in the on or off position. Measurement of quality of service (QoS) carried out in this study is the measurement of throughput and delay. The throughput measurement is carried out to determine the bandwidth used in the Argotech system. Throughput measurements were carried out three times with the results obtained in the morning at 2926.2 bps, in the afternoon at 2926.3 and night at 2943.4 bps. Agrotech's system delay is 483-487 ms. This study concludes that the Argotech system works well, does not require large bandwidth and has a relatively small delay. | Paper Link |
| WILMA CHRISTY NATALIA, FIKY YOSEF SURATMAN, ALOYSIUS ADYA PRAMUDITA | Connectivity & Convergence Instruments for Smart Living | FMCW Radar Post Processing Method for Small Displacement Detection | A conventional Frequency Modulated Continuous Wave (FMCW) radar needs a large bandwidth in detecting a small displacement with millimeter scale. Phase data processing usually was implemented to avoid the needs of large bandwidth in radar system. Many implementations of phase detection in FMCW, generally utilized the IQ demodulation that placed at radio frequency (RF) circuit which a significant change is needed in RF circuit part. This paper proposes a method of advance post processing method in FMCW radar for small displacement detection. In the proposed method, IQ demodulation is performed at computation domain to minimize the change in RF hardware meanwhile the beat frequency detection using Fast Fourier Transform (FFT) still elaborate in the proposed post processing to detect the initial position of the target. Theoretical and simulation analysis has performed to investigate the ability of proposed method and the results show that the proposed method is able to detect a small displacement in millimeter scale without increasing the FMCW bandwidth and modification in RF hardware. | Paper Link |
| YUDHISTIRA NUGRAHA, ALEX LUKMANTO SUHERMAN | Connectivity & Convergence Instruments for Smart Living | Monthly Rainfall Prediction Using the Facebook Prophet Model for Flood Mitigation in Central Jakarta | Jakarta has been known as the city where floods are prevalent. As the vital region in Jakarta where the center of government and business are located, Central Jakarta is inseparable from the flood when the rainfall is remarkably high. Therefore, the Jakarta Provincial Government need a data-driven policy to facing potential flood that may occur each year to protect the citizen from the threat of flood disaster. Monthly rainfall prediction can be a reference to determine the possibility of considerable loss and damage due to disaster threats. However, at this moment, it is still challenging to find a fitting forecasting model for this context. This paper reports a comparison of three different time series models: Seasonal Autoregressive Integrated Moving Average (SARIMA), Facebook Prophet, and Long Short-Term Memory (LSTM) to forecast monthly rainfall in Central Jakarta for up to two consecutive years. The result indicates that Facebook Prophet, with the lowest Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), is the fittest model to predict the monthly rainfall in Central Jakarta. It shows that a high amount of rainfall will be seen in January and February 2021, which suggests we need to be prepared to anticipate the potential flood. Facebook Prophet shows promising results in supporting data-driven policy for flood mitigation in Jakarta. The development of this model in the future can be used as a baseline study to formulate a data-driven policy for flood mitigation in Jakarta. | Paper Link |
| YUDHISTIRA NUGRAHA, ANDI SULASIKIN, MUHAMMAD ERZA AMINANTO, BAHRUL ILMI NASUTION, JUAN INTAN KANGGRAWAN | Connectivity & Convergence Instruments for Smart Living | Developing a knowledge management system for supporting flood decision-making | Flood has been a critical issue for many cities, especially Jakarta, due to geographic structure, weather, and citizen behaviors. The United Nations have assigned disaster management as one of the Sustainable Development Goals. However, many cities in developing countries still face challenges in implementing comprehensive flood management services. In this study, we elaborate on efforts that have been made to achieve a one-stop flood management service, using Jakarta as a case study. The proposed methodologies have three values: sensing, understanding, and acting. First, we build sensors to measure various flood parameters in the sensing approach. Second, we collect all data output from the previous system to provide robust analysis in the understanding part. Finally, in the acting module, we provide a dashboard for the decision support system in the flood management system. Although the system has been established, several challenges to achieving a comprehensive flood management system are clarified in this study, especially data management and governance issues. We conclude this study with the future applications of the flood management system that can be expected to minimize the risk of flood disasters in Jakarta. | Paper Link |
| Connectivity & Convergence Instruments for Smart Living | Digital Board Game Design for English Vocabulary???s Learning Tool During Studying from Home | English language learners often comment that learning English vocabulary is a challenge due to spelling, pronunciation, parts of speech, and a variety of meanings. Over the years, using educational games for classroom activities has been a popular and effective way to teach since it brings fun and relaxation, creates a friendly environment for competition, and keeps learners interested in learning. Therefore, this research aims to develop a digital learning tool called SQUARE TALKS!, a digital board game for English vocabulary learning focusing on the design phase involving language experts, game designers, and software developers. This current research explores the design phase from the game designers’ perspectives. Qualitative methods are used in this research since the research does some observations and content analysis for data collecting. The results of the game development phase covered four aspects: narration, mechanic, aesthetics, and technology. The digital game prototype is envisioned to be an alternative learning tool to use game-based learning to assist learners and teachers during the recent COVID-19 pandemic situation. | Paper Link |