Pub Date : 2022-07-06DOI: 10.1109/ICoDSA55874.2022.9862855
Muhammad Fadhil Ihsan, Satria Mandala, M. Pramudyo
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.
{"title":"Study of Feature Extraction Algorithms on Photoplethysmography (PPG) Signals to Detect Coronary Heart Disease","authors":"Muhammad Fadhil Ihsan, Satria Mandala, M. Pramudyo","doi":"10.1109/ICoDSA55874.2022.9862855","DOIUrl":"https://doi.org/10.1109/ICoDSA55874.2022.9862855","url":null,"abstract":"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.","PeriodicalId":339135,"journal":{"name":"2022 International Conference on Data Science and Its Applications (ICoDSA)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123417901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-06DOI: 10.1109/ICoDSA55874.2022.9862820
Hanif Fadhlurrahman, Azka Khoirunnisa, I. Kurniawan
Diabetes mellitus or diabetes is a kind of disease characterized by a raised in blood sugar. This disease can deal with long-term damage, such as dysfunction and failure of various organs. In Indonesia, diabetes is one of the major causes of death, with more than 10 million people living with diabetes. To date, no drug can cure diabetes. So far, people with diabetes must take responsibility for their daily routine. Drug discovery is needed to find the cure for diabetes. protein tyrosine phosphatase 1B (PTP1B) is one inhibitor that proved as a promising target for anti-diabetes Mellitus. Drug discovery takes a lot of time and effort, and thus, in silico methods, such as quantitative structure-activity relationship (QSAR), can be used to accelerate this process. We aim to build a QSAR model of PTP1B inhibitor as anti-diabetes Mellitus using the simulated annealing (SA)-Support Vector Machine (SVM) method. The data were retrieved from the ChEMBL database by selecting the SMILES from each compound. By calculating the SMILES using PaDEL, we got 1443 descriptors for each compound, and by using SA, we decreased the number of descriptors. The best result shows that SA selected 600 descriptors out of 1443 descriptors for each compound. The RBF kernel on SVM has the best value with accuracy, F1 score, and AUC of 94.508%, 95.048%, and 0.943, respectively.
{"title":"QSAR Model for Prediction PTP1B Inhibitor as Anti-diabetes Mellitus using Simulated Annealing-Support Vector Machine","authors":"Hanif Fadhlurrahman, Azka Khoirunnisa, I. Kurniawan","doi":"10.1109/ICoDSA55874.2022.9862820","DOIUrl":"https://doi.org/10.1109/ICoDSA55874.2022.9862820","url":null,"abstract":"Diabetes mellitus or diabetes is a kind of disease characterized by a raised in blood sugar. This disease can deal with long-term damage, such as dysfunction and failure of various organs. In Indonesia, diabetes is one of the major causes of death, with more than 10 million people living with diabetes. To date, no drug can cure diabetes. So far, people with diabetes must take responsibility for their daily routine. Drug discovery is needed to find the cure for diabetes. protein tyrosine phosphatase 1B (PTP1B) is one inhibitor that proved as a promising target for anti-diabetes Mellitus. Drug discovery takes a lot of time and effort, and thus, in silico methods, such as quantitative structure-activity relationship (QSAR), can be used to accelerate this process. We aim to build a QSAR model of PTP1B inhibitor as anti-diabetes Mellitus using the simulated annealing (SA)-Support Vector Machine (SVM) method. The data were retrieved from the ChEMBL database by selecting the SMILES from each compound. By calculating the SMILES using PaDEL, we got 1443 descriptors for each compound, and by using SA, we decreased the number of descriptors. The best result shows that SA selected 600 descriptors out of 1443 descriptors for each compound. The RBF kernel on SVM has the best value with accuracy, F1 score, and AUC of 94.508%, 95.048%, and 0.943, respectively.","PeriodicalId":339135,"journal":{"name":"2022 International Conference on Data Science and Its Applications (ICoDSA)","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128172763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-06DOI: 10.1109/ICoDSA55874.2022.9862923
Moch Rafi Nur Octafian, L. Novamizanti, Irma Safitri, Richardo Praystihan Sitepu
The presence of digital devices and the internet has made information and communication technologies rapidly expanding. Security and confidentiality-related technologies are still much complained of by many to date, while technology has brought enormous benefits to the interests of individuals and groups. In this case, security and confidentiality are essential addressed. Therefore, steganography is one solution to the problem. In this study, a steganography audio system was designed with the disrete cosine transform-stationary wavelet transform (DCT-SWT) encrypted method of RC4. Messages before being inserted first performed the encryption process with the RC4 algorithm to increase message security. DCT changes the signal at the time domain into the frequency domain, and the SWT decomposes the signals into low and high-frequency sub-bands. The performance of the steganography audio system is analyzed and measured based on quality parameters. After the entire attack, the process optimization of the parameters is conducted by evaluating the high BER value. The proposed audio steganography technique is resistant to LPF, BPF, Resampling, TSM, and LSC attacks. Messages with RC4 and without RC4 have a similar quality value, which means that RC4 does not significantly inhibit a steganography audio system's quality value and performance. Many character messages significantly affect the performance of a steganography audio system. The more the character of the message, the less the quality and the performance of a steganography audio system.
{"title":"Audio Steganography Technique using DCT-SWT with RC4 Encryption","authors":"Moch Rafi Nur Octafian, L. Novamizanti, Irma Safitri, Richardo Praystihan Sitepu","doi":"10.1109/ICoDSA55874.2022.9862923","DOIUrl":"https://doi.org/10.1109/ICoDSA55874.2022.9862923","url":null,"abstract":"The presence of digital devices and the internet has made information and communication technologies rapidly expanding. Security and confidentiality-related technologies are still much complained of by many to date, while technology has brought enormous benefits to the interests of individuals and groups. In this case, security and confidentiality are essential addressed. Therefore, steganography is one solution to the problem. In this study, a steganography audio system was designed with the disrete cosine transform-stationary wavelet transform (DCT-SWT) encrypted method of RC4. Messages before being inserted first performed the encryption process with the RC4 algorithm to increase message security. DCT changes the signal at the time domain into the frequency domain, and the SWT decomposes the signals into low and high-frequency sub-bands. The performance of the steganography audio system is analyzed and measured based on quality parameters. After the entire attack, the process optimization of the parameters is conducted by evaluating the high BER value. The proposed audio steganography technique is resistant to LPF, BPF, Resampling, TSM, and LSC attacks. Messages with RC4 and without RC4 have a similar quality value, which means that RC4 does not significantly inhibit a steganography audio system's quality value and performance. Many character messages significantly affect the performance of a steganography audio system. The more the character of the message, the less the quality and the performance of a steganography audio system.","PeriodicalId":339135,"journal":{"name":"2022 International Conference on Data Science and Its Applications (ICoDSA)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129902541","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-07-06DOI: 10.1109/ICoDSA55874.2022.9862869
Mohammad Daffa Haris, D. Adytia, Annas Wahyu Ramadhan
The high number of industrial and residential areas has reduced green space in Jakarta. This condition increases air temperature, contributing to climate change in Jakarta and most other big cities in Indonesia. Therefore, an accurate air temperature prediction model is needed to support daily public activities. On the other hand, the government can also use this prediction to determine regulations to suppress climate change. This study developed Jakarta’s air temperature prediction model using two machine learning models: Long Short-Term Memory (LSTM) and Prophet. LSTM is a variant of the classic Recurrent Neural Networks (RNN) with the addition of memory blocks that stores long-term information. The Prophet is an additive regression model developed by Facebook. These models are chosen to handle stochastic data such as air temperature. Here, we forecast the time series of air temperature based on sequential historical data. The accuracy of prediction is measured by using RMSE and Correlation Coefficient values. Results of the study indicate that the LSTM performs better for short-term forecasts, i.e., 2 to 48 hours, with RMSE values between 0.31 to 0.69. On the other hand, the Prophet model is suitable for more long-term predictions, i.e., 72 to 168 hours, with RMSE between 0.80 and 0.89.
{"title":"Air Temperature Forecasting with Long Short-Term Memory and Prophet: A Case Study of Jakarta, Indonesia","authors":"Mohammad Daffa Haris, D. Adytia, Annas Wahyu Ramadhan","doi":"10.1109/ICoDSA55874.2022.9862869","DOIUrl":"https://doi.org/10.1109/ICoDSA55874.2022.9862869","url":null,"abstract":"The high number of industrial and residential areas has reduced green space in Jakarta. This condition increases air temperature, contributing to climate change in Jakarta and most other big cities in Indonesia. Therefore, an accurate air temperature prediction model is needed to support daily public activities. On the other hand, the government can also use this prediction to determine regulations to suppress climate change. This study developed Jakarta’s air temperature prediction model using two machine learning models: Long Short-Term Memory (LSTM) and Prophet. LSTM is a variant of the classic Recurrent Neural Networks (RNN) with the addition of memory blocks that stores long-term information. The Prophet is an additive regression model developed by Facebook. These models are chosen to handle stochastic data such as air temperature. Here, we forecast the time series of air temperature based on sequential historical data. The accuracy of prediction is measured by using RMSE and Correlation Coefficient values. Results of the study indicate that the LSTM performs better for short-term forecasts, i.e., 2 to 48 hours, with RMSE values between 0.31 to 0.69. On the other hand, the Prophet model is suitable for more long-term predictions, i.e., 72 to 168 hours, with RMSE between 0.80 and 0.89.","PeriodicalId":339135,"journal":{"name":"2022 International Conference on Data Science and Its Applications (ICoDSA)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116730185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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