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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) International Journal of Advances in Applied Sciences JLBG (Jurnal Lingkungan dan Bencana Geologi) (Journal of Environment and Geological Hazards) Jurnal Teknologi
Pratama, Thomas Oka
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics

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Earthquake magnitude prediction in Indonesia using a supervised method based on cloud radon data Pratama, Thomas Oka; Sunarno, Sunarno; Wijatna, Agus Budhie; Haryono, Eko
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 13, No 3: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v13.i3.pp577-585

Abstract

In the challenging realm of earthquake prediction, the reliability of forecasting systems has remained a persistent obstacle. This study focuses on earthquake magnitude prediction in Indonesia, leveraging supervised machine learning techniques and cloud radon data. We present an analysis of the tele-monitoring system, data collection methods, and the application of regression-based machine learning algorithms. Utilizing a comprehensive dataset spanning 30 training instances and 105 test instances, the study evaluates multiple metrics to ascertain the efficacy of the prediction models. Our findings reveal that the linear regression approach yields the best earthquake magnitude prediction method, with the lowest values across multiple evaluation metrics: standard deviation 0.40, mean absolute error (MAE) 0.30, mean absolute percentage error (MAPE) 6%, root mean square error (RMSE) 0.52, mean squared error (MSE) 0.28, symmetric mean absolute percentage error (SMAPE) 0.06, and conformal normalized mean absolute percentage error (cnSMAPE) 0.97. Additionally, we discuss the implications of the research results and the potential applications in enhancing existing earthquake prediction methodologies.
Earthquake Date Prediction Based on The Fluctuation of Radon Gas Concentration Near Grundulu Fault Pratama, Thomas Oka; Sunarno, Sunarno; Waruwu, Memory Motivanisman; Wijaya, Rony
Jurnal Lingkungan dan Bencana Geologi Vol 14, No 2 (2023)
Publisher : Badan Geologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34126/jlbg.v14i2.478

Abstract

There were 5 to 26 destructive earthquakes from 2020 to 2022 in Indonesia. Earthquake prediction is unsuccessful and has not provided a reliable forecasting mechanism. At the same time, it is necessary to have an earthquake early warning system to reduce the risk of an accident. In Indonesia, radon gas has been studied to determine its relationship with earthquake events, but earthquake predictions have low sensitivity and accuracy values. In this study, the prediction of earthquake time was carried based on radon gas concentration fluctuations in the active Grundulu fault, which is located in Pacitan, East Java, Indonesia. The method is to collect radon gas concentration measurement data from telemonitoring stations near active faults. The data is then sent to the web server and processed based on the daily average. The daily average of radon gas concentrations and earthquake occurrences is tabulated by day. The daily average data for the concentration of radon gas that is processed is when an earthquake occurs between the Eurasian and Indo-Australian plates with a magnitude of more than M4.5. After that, the daily average radon gas concentrations were statistically processed to find the earthquake time prediction algorithm. The study's findings show that earthquakes above M4.5 that occur between the Eurasian and Indo-Australian plates can be predicted using statistical data processing from radon gas concentration measurements near the Grundulu fault, Pacitan, 1-4 days before the earthquake. The earthquake date prediction algorithm developed has a sensitivity and precision of 78.79% and 70.27 %. This achievement is better than previous research that predicts the time of earthquakes near the Opak Fault, Yogyakarta.Kata kunci: Active Fault, Earthquake, Prediction, Radon, Telemonitoring
Grindulu fault cloud radon data for earthquake magnitude prediction using machine learning Pratama, Thomas Oka; Sunarno, Sunarno; Wijatna, Agus Budhie; Haryono, Eko
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i4.pp4572-4582

Abstract

The study investigates the potential of integrating radon gas concentration telemonitoring systems with machine learning techniques to enhance earthquake magnitude prediction. Conducted in Pacitan, East Java, Indonesia, where the stations are near the active Grindulu fault, the research employs random forest (RF), extreme gradient boosting (XGB), neural network (NN), AdaBoost (AB), and support vector machine (SVM) methods. The study aims to refine earthquake magnitude prediction, utilizing real-time radon gas concentration measurements, crucial for disaster preparedness. The evaluation involves multiple metrics like mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), mean squared error (MSE), symmetric mean absolute percentage error (SMAPE), and conformal normalized mean absolute percentage error (cnSMAPE). XGB and SVM emerge as top performers, showcasing superior predictive accuracy with minimal errors across various metrics. XGB achieved MAE (0.33), MAPE (6.03%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97), while SVM recorded MAE (0.34), MAPE (6.20%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnSMAPE (0.97). The analysis reveals XGB as the most effective method, boasting the lowest error values. The study underscores the importance of expanding data availability to enhance predictive models, ultimately contributing to more precise earthquake magnitude predictions and effective mitigation strategies.
Earthquake epicenter prediction from the Java-Bali radon gas telemonitoring station using machine learning Putranto, Christophorus Arga; Sunarno, Sunarno; Faridah, Faridah; Pratama, Thomas Oka
International Journal of Advances in Applied Sciences Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i1.pp39-45

Abstract

Predicting the location of earthquake epicenters is a critical aspect of earthquake forecasting, as it complements efforts to determine the time and magnitude of seismic events. This research addresses the challenge posed by the uncertainty in epicenter locations, particularly along the extensive plate faults of Indo-Australia and Eurasia. In these regions, effective earthquake prediction is compromised without accurate epicenter information, impeding mitigation strategies and complicating disaster impact estimation. The primary objective of this study is to devise an algorithm for forecasting earthquake epicenter locations by harnessing variations in radon gas concentrations on southern Java Island, Indonesia, as a predictive precursor. Using a supervised machine learning approach, this study integrates radon gas concentration data to predict the distance between a radon gas telemonitoring station and the impending earthquake epicenter. Three distinct machine learning algorithms were evaluated using data from six Java-Bali radon gas telemonitoring stations within an early warning system. The random forest algorithm emerged as the most effective, yielding an average root mean square error of 453.10 kilometers. The findings of this research significantly contribute to earthquake risk mitigation efforts. This work enhances our capability to anticipate seismic events, and more effective disaster preparedness and response strategies in earthquake-prone regions.
Machine Learning-Based Prediction of Sleep Disorders from Lifestyle and Physiological Data: A Cross-Occupational Study Sari, Hermin Kartika; Shoelarta, Shoerya; Pratama, Thomas Oka; Sajida, Gita Nur; Krista, Gustin Mustika; Ferawati, Yohana Fransiska; Taufiqurrahim, Teguh
Jurnal Teknologi Vol 25, No 2 (2025): Agustus 2025
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/teknologi.v25i2.7507

Abstract

Sleep disorders are increasingly recognized as critical public health concerns, particularly among working populations where occupational stress, lifestyle factors, and physiological imbalances intersect. This study explores the predictive capacity of machine learning models, including Random Forest, Support Vector Machine (SVM), and XGBoost to identify sleep disorders (None, Insomnia, and Sleep Apnea) using a dataset comprising demographic, occupational, lifestyle, and physiological variables. The dataset, drawn from 400 individuals, was preprocessed through normalization, one-hot encoding, and SMOTE to address class imbalance. Feature selection was conducted using correlation analysis, RFE, and Random Forest importance scores. Models were trained with stratified sampling and optimized using 5-fold cross-validation. XGBoost outperformed the others with an accuracy of 0.90 and an F1-score of 0.88, followed by Random Forest (0.875, 0.86), while SVM lagged (0.825, 0.71). Confusion matrix analysis revealed consistent misclassification between Insomnia and Sleep Apnea, reflecting overlapping symptomatology and low feature correlation. Occupational analysis showed that manual laborers exhibited higher stress levels and shorter sleep durations, particularly those with insomnia. These findings highlight the value of integrating occupational and physiological data into predictive modeling and underscore the potential of ensemble learning methods in health informatics. This study supports the development of early detection systems for sleep disorders tailored to occupational risk profiles.
Co-Authors Agus Budhie Wijatna Eko Haryono Faridah Faridah Ferawati, Yohana Fransiska Krista, Gustin Mustika Memory Motivanisman Waruwu Putranto, Christophorus Arga Rony Wijaya Sajida, Gita Nur Sari, Hermin Kartika Shoelarta, Shoerya Sunarno Sunarno Taufiqurohim, Teguh