Perbandingan Kinerja Random Forest dan Long Short-Term Memory dalam Prediksi Kedalaman Hiposentrum Gempa di Maluku dan Laut Banda
DOI:
https://doi.org/10.31599/5a5m1q14Keywords:
Earthquakes, Hypocenter Depth, LSTM, Maluku and Banda Sea, Random ForestAbstract
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.
Downloads
References
Gaol, Y. H. L., & Suhardja, S. K. (2022). Hasil Awal Penentuan Waktu Tiba Gelombang-P Gempa Menggunakan Machine Learning. Jurnal Geofisika, 20(02), 76–80. https://doi.org/10.36435/jgf.v20i2.538
Hutchings, S. J., & Mooney, W. D. (2021). The Seismicity of Indonesia and Tectonic Implications. Geochemistry, Geophysics, Geosystems, 22(9), 1–42. https://doi.org/10.1029/2021GC009812
Larasati, K. D., & Primandari, A. H. (2021). Forecasting Bitcoin Price Based on Blockchain Information Using Long-Short Term Method. Parameter: Journal of Statistics, 1(1), 1–6. https://doi.org/10.22487/27765660.2021.v1.i1.15389
Mart, F., Contreras-ochando, L., & Lachiche, N. (2021). CRISP-DM Twenty Years Later : From Data Mining Processes to Data Science Trajectories. 33, 3048–3061.
Maulita, I., Mu, A., Fisika, J., Mipa, F., Soedirman, U. J., Komputer, M. I., Komputer, F. I., & Purwokerto, U. A. (2024). Prediksi Magnitudo Gempa Menggunakan Random Forest, Support Vector Regression, XGBoost, LightGBM, dan Multi-Layer Perceptron Berdasarkan Data Kedalaman dan Geolokasi. Jurnal Pendidikan Dan Teknologi Indonesia (JPTI), 4(5), 221–232. https://doi.org/10.52436/1.ipti.470
Melani, P., Realita, A., & Prastowo, T. (2024). Analisis Seismisitas Maluku Dan Barat Papua Berdasarkan Distribusi Frekuensi-Magnitudo Gempa. Jurnal Inovasi FIsika Indonesia (IFI), 13, 25–36. https://doi.org/10.26740/ifi.v13n2.p25-36
Putri, M. A., Suhendra, R., & Ridho, A. (2025). Jurnal Teknologi Informasi Analisis Kinerja Algoritma Long Short-Term Memory ( LSTM ) untuk Prediksi Gempa Bumi di Aceh. Jurnal Teknik Informasi, 4(2), 8–18. https://doi.org/10.35308/jti.v4i2.12921
Rachman, G., Santosa, B. J., Nugraha, A. D., Rohadi, S., Rosalia, S., Zulfakriza, Z., Sungkono, S., Sahara, D. P., Muttaqy, F., Supendi, P., Ramdhan, M.,
Ardianto, A., & Afif, H. (2022). Seismic Structure Beneath the Molucca Sea Collision Zone from Travel Time Tomography Based on Local and Regional BMKG Networks. Applied Sciences (Switzerland), 12(20). https://doi.org/10.3390/app122010520
Rianti, A., Wachid, N., Majid, A., & Fauzi, A. (2023). CRISP-DM : Metodologi Proyek Data Science. 107–114.
Saputra, D. B., Atina, V., & Nastiti, F. E. (2024). Penerapan Model Crisp-Dm Pada Prediksi Nasabah Kredit Menggunakan Algoritma Random Forest. IDEALIS : InDonEsiA JournaL Information System, 7(2), 240–247. https://doi.org/10.36080/idealis.v7i2.3244
Sarker, I. H. (2021). Machine Learning: Algorithms, Real-World Applications and Research Directions. SN Computer Science, 2(3). https://doi.org/10.1007/s42979-021-00592-x
Sianturi, T. B., Cholissodin, I., & Yudistira, N. (2023). Penerapan Algoritma Long Short-Term Memory (LSTM) berbasis Multi Fungsi Aktivasi Terbobot dalam Prediksi Harga Ethereum. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 7(3), 1101–1107. http://j-ptiik.ub.ac.id
Xu, M., Zhao, L., Liu, M., & Geng, J. (2025). Machine Learning‐Based Seismic Subsurface Characterization: The State of the Art and Future Perspectives. Journal of Geophysical Research: Machine Learning and Computation, 2(4), 1–37. https://doi.org/10.1029/2025jh000846
Yudistira, N., Alfiansih, M. D., Andriyani, N. I., Essayem, W., Maghfiroh, N. A., & Nurdian, I. W. (2025). Prediksi Deret Waktu Menggunakan Deep Learning (Vol. 6, Issue 0).
_-_Copy1.jpg)
