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Abstract
East Java is one of the seismically active regions in Indonesia, yet predictive studies that integrate spatial data and event parameters remain limited. This study develops a two-stage approach to model earthquake risk more comprehensively by combining Bayesian inference and logistic regression. The first stage employs a Bayesian model to estimate the daily probability of earthquake occurrence based on historical data from 2014 to 2024. The results show an average daily probability of 13.5%, with a 95% credible interval indicating a high level of confidence. Spatially, Region 1 (covering southern East Java) is identified as the area with the highest probability, followed by Region 3 and Region 2. In the second stage, logistic regression is used to identify combinations of event parameters—particularly magnitude and depth—that significantly influence the likelihood of moderate-to-major earthquakes (magnitude ≥ 5.0). The prediction results indicate that most high-risk events occur at shallow depths in Region 1 and Region 3, while Region 2 appears less frequently but still presents underlying geological hazards. These findings demonstrate that integrating probabilistic modeling with parameter-based classification offers a more refined understanding of earthquake risk. As an initial framework, this study also opens avenues for developing future early warning systems based on dynamic data and machine learning methods.
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Copyright (c) 2025 Dr. Siti Amiroch, S.Si, M.Si
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L. Irawan, L. H. Hasibuan, and Fauzi, “Analisa Prediksi Efek Kerusakan Gempa dari Magnitudo (Skala Richter) dengan Metode Algoritma ID3 Menggunakan Aplikasi Data Mining Orange,” Jurnal Teknologi Informasi, vol. 14, no. 2, pp. 189–201, 2020, doi: https://doi.org/10.47111/jti.v14i2.1079.
D. A. Pramadhana, S. T. Rinanda, and Y. D. Haryanto, “Studi Karakteristik Sesar Bawean ( Wilayah Laut Utara Jawa ) menggunakan Analisis Turunan dengan Data Gaya Berat,” Sosial : Jurnal Ilmiah Pendidikan IPS, vol. 3, no. 1, pp. 39–53, 2025, doi: https://doi.org/10.62383/sosial.v3i1.587.
A. F. Amri and Ikhya, “Perbandingan Peta Gempa pada Analisis Potensi Likuefaksi ( Studi Kasus Jalan Tol Ruas Probolinggo – Banyuwangi Seksi II ),” RekaRacana: Jurnal Teknik Sipil, vol. 6, no. 2, pp. 64–74, 2020, doi: https://doi.org/10.26760/rekaracana.v6i2.64.
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U. Chasanah and E. Handoyo, “Analisis Tingkat Kegempaan Wilayah Jawa Timur Berbasis Distribusi Spasial dan Temporal Magnitude of Completeness (Mc), A-Values dan B-Value,” Indonesian Journal of Applied Physics, vol. 11, no. 2, pp. 210–222, 2021, doi: https://doi.org/10.13057/ijap.v11i2.45984.
A. A. Abdullah, M. M. Hassan, and Y. T. Mustafa, “Leveraging Bayesian deep learning and ensemble methods for uncertainty quantification in image classification : A ranking-based approach,” Heliyon, vol. 10, no. 2, pp. 1–12, 2024, doi: 10.1016/j.heliyon.2024.e24188.
Y. Idrissi, T. Richeton, D. Texier, S. Berbenni, and J. Lecomte, “Robust determination of cubic elastic constants via nanoindentation and Bayesian inference,” Acta Materialia, vol. 281, no. June, p. 120406, 2024, doi: 10.1016/j.actamat.2024.120406.
S. C. Jong and D. E. L. Ong, “A Bayesian inference framework for geomaterial characterization and evaluation of complex soil-structure interactions,” Computers and Geotechnics, vol. 172, no. May, p. 106452, 2024, doi: 10.1016/j.compgeo.2024.106452.
E. A. Surijah and I. M. F. Anggara, “Bayesian Statistics in Psychological Research,” JP3I: Jurnal Pengukuran Psikologi dan Pendidikan Indonesia, vol. 10, no. 2, pp. 99–117, 2021, doi: https://doi.org/10.15408/jp3i.v10i2.20185.
S. Amiroch, M. Jamhuri, M. I. Irawan, I. Mukhlash, and C. A. Nidom, “Predicting Antiviral Compounds for Avian Influenza A/H9N2 Using Logistic Regression with RBF Kernel,” in 2023 International Conference on Modeling & E-Information Research, Artificial Learning and Digital Applications (ICMERALDA), 2023, pp. 68–73. doi: https://doi.org/10.1109/ICMERALDA60125.2023.10458186.
F. G. Habtewold, A. T. Goshu, and B. G. Arero, “Bayesian Inference for the Beta ‑ Weibull Distribution with Applications to Cancer and Under ‑ nutrition Data,” Journal of Statistical Theory and Applications, vol. 24, no. 1, pp. 160–198, 2025, doi: 10.1007/s44199-025-00106-1.
T. A. Musana, F. Yanuar, and Y. Asdi, “Identifikasi Distribusi Jumlah Kecelakaan Lalu Lintas di Depok dan Pendugaan Parameternya Menggunakan Metode Bayes,” Jurnal Matematika UNAND, vol. 8, no. 4, pp. 62–68, 2019, doi: https://doi.org/10.25077/jmu.8.4.62-68.2019.
Ermawati, R. Ibnas, and B. A. Kurniawan, “Klasifikasi Penderita Anemia Menggunakan Metode Regresi Logistik,” Jurnal Matematika dan Statistika serta Aplikasinya, vol. 11, no. 2, pp. 93–101, 2023, doi: https://doi.org/10.24252/msa.v11i2.45083.
L. I. Harlyan, E. S. Yulianto, Y. Fitriani, and Sunardi, “Aplikasi Akaike Information Criterion (AIC) Pada Perhitungan Efisiensi Teknis Perikanan Pukat Cincin Di Tuban, Jawa Timur,” Marine Fisheries, vol. 11, no. 2, pp. 181–188, 2020, doi: https://doi.org/10.29244/jmf.v11i2.38550.
G. Mardiatmoko, “Pentingnya Uji Asumsi Klasik Pada Analisis Regresi Linier Berganda (Studi Kasus Penyusunan Persamaan Allometrik Kenari Muda [Canarium Indicum L.]),” Barekeng: Jurnal Ilmu Matematika dan Terapan, vol. 14, no. 3, pp. 333–342, 2020, doi: https://doi.org/10.30598/barekengvol14iss3pp333-342.