Perbandingan Kinerja Random Forest dan Long Short-Term Memory dalam Prediksi Kedalaman Hiposentrum Gempa di Maluku dan Laut Banda

Laila Salsabilla Hanifa (Universitas Bhayangkara Jakarta Raya)
Herlawati Herlawati (Universitas Bhayangkara Jakarta Raya)
Rakhmat Purnomo (Universitas Bhayangkara Jakarta Raya)

Journal of Students‘ Research in Computer Science (JSRCS)
Vol. 7 No. 1 (2026): Mei 2026

Hypocenter depth is an important parameter in earthquake analysis because it is related to the earthquake source mechanism and its impact on the Earth’s surface. The Maluku and Banda Sea regions have high seismic activity caused by interactions among active tectonic plates. This study compares the performance of Random Forest and Long Short-Term Memory (LSTM) algorithms in predicting earthquake hypocenter depth using historical earthquake data from the United States Geological Survey (USGS). Model evaluation used MAE, RMSE, and R² metrics. The results show that Random Forest outperformed LSTM, achieving MAE, RMSE, and R² values of 31.94, 62.83, and 0.73, respectively. Meanwhile, LSTM produced MAE of 69.35, RMSE of 119.31, and R² of -0.11. Therefore, Random Forest was considered more effective for predicting earthquake hypocenter depth in the Maluku and Banda Sea regions.