William Hilmy Susatyo
Research Subjects
Research Interests
William Hilmy Susatyo has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Audrey Shafira Fattima
Program Studi Ilmu Komputer FMIPA UGM, Yogyakarta
1 shared publication - Dzikri Rahadian Fudholi
Universitas Gadjah Mada
1 shared publication - Rabbani Nur Kumoro
Program Studi Ilmu Komputer FMIPA UGM, Yogyakarta
1 shared publication
Latest Publications
- Open Access
- 31 July 2024
- Published by Universitas Gadjah Mada in IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
- Vol. 18 (3)
Analysis and Prediction of the Occurrence of an Earthquake Using ARIMA and Statistical Tests
Earthquakes present significant risks to both human safety and infrastructure, emphasizing the need for precise prediction models to minimize their adverse effects. This study seeks to tackle the challenge of accurately forecasting the occurrence time of earthquakes by utilizing the LANL Earthquake dataset, which comprises seismic signals from a laboratory model emulating tectonic faults. In this study, we employed the ARIMA model and compared it with Linear Regression to predict earthquake occurrences. Our findings demonstrate that the ARIMA (1,1,1) model surpasses other models, achieving the lowest MAE of 0.110628. The validity of the model's assumptions is confirmed through the Ljung-Box and Jarque-Bera tests, which verify the absence of autocorrelation and the normal distribution of residuals, respectively.
- 31 July 2024
- Published by Universitas Gadjah Mada in IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
- Vol. 18 (3)
Open AccessAnalysis and Prediction of the Occurrence of an Earthquake Using ARIMA and Statistical Tests
Earthquakes present significant risks to both human safety and infrastructure, emphasizing the need for precise prediction models to minimize their adverse effects. This study seeks to tackle the challenge of accurately forecasting the occurrence time of earthquakes by utilizing the LANL Earthquake dataset, which comprises seismic signals from a laboratory model emulating tectonic faults. In this study, we employed the ARIMA model and compared it with Linear Regression to predict earthquake occurrences. Our findings demonstrate that the ARIMA (1,1,1) model surpasses other models, achieving the lowest MAE of 0.110628. The validity of the model's assumptions is confirmed through the Ljung-Box and Jarque-Bera tests, which verify the absence of autocorrelation and the normal distribution of residuals, respectively.