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All Journal Charm Sains : Jurnal Pendidikan Fisika Edu Komputika Journal
Muhammad Zulkifli
Badan Meteorologi Klimatologi dan Geofisika
Education Physics

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ANALISIS BAHAYA BENCANA GEMPABUMI DI WILAYAH BITUNG MENGGUNAKAN METODE PROBABILITY SEISMIC HAZARD ANALYSIS Pamela Sifra Lumingas; Heinrich Taunaumang; Farly Tumimomor; Muhammad Zulkifli
Charm Sains: Jurnal Pendidikan Fisika Vol 1 No 3 (2020): OKTOBER
Publisher : Program Studi Pendidikan Fisika Jurusan Fisika Fakultas Matematika, Ilmu Pengetahuan Alam, dan Kebumian (FMIPAK), Universitas Negeri Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1216.438 KB) | DOI: 10.53682/charmsains.v1i3.49

Abstract

Bitung district was an active seismic area in North Sulawesi Province. This is because of platetectonic activity, among other things, subduction of West Molucca Sea and subduction of NorthSulawesi Trench that can cause earthquake disaster. Earthquake is an unpredictable disaster thatcan pose a danger to a region. So the purpose of the research is to minimize the impact of theearthquake disaster. The study conducted with study of the earthquake hazard analytics usingprobability approaches with 500-year-old return period, probability exceeded 10% in 50 years ofbuilding age in condition T= 0 second, T= 0.2 second and T= 0.1 second. PSHA’s calculation in theBitung region were obtained at PGA value of the same amount 1.4g – 1.8g, SA value T= 0.2 secondis 1,7g-2,1g and SA value T= 1,0 second is 0,5-0,6g. as for the turbulence of the result of teconversion of the PGA value being on the VIII-IX MMI scale which means it can cause a seriousrisk of damage to the building.
RELOKASI HIPOSENTER MENGGUNAKAN METODE MODIFIED JOINT HYPOCENTER DETERMINATION (STUDI KASUS GEMPABUMI LAUT MALUKU TANGGAL 15 NOVEMBER 2019) Sisca Cicilya Sabonbali; Heinrich Taunaumang; Ferdy Dungus; Muhammad Zulkifli; Sesar Prabu Dwi Sriyanto
Charm Sains: Jurnal Pendidikan Fisika Vol 1 No 3 (2020): OKTOBER
Publisher : Program Studi Pendidikan Fisika Jurusan Fisika Fakultas Matematika, Ilmu Pengetahuan Alam, dan Kebumian (FMIPAK), Universitas Negeri Manado

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (728.833 KB) | DOI: 10.53682/charmsains.v1i3.50

Abstract

In this study, we determined the accurate hypocenter location of November 15, 2019, the Molucca Sea earthquake aftershocks using the Modified Joint Hypocenter Determination (MJHD) method. This method calculated the hypocenter and seismic station correction simultaneously using the seismic wave travel time inversion. The P and S wave arrival time at each station from the BMKG database was used in this study. After relocated, most of the aftershocks hypocenter categorized as shallow and medium depth earthquakes with the range of focal depth between 25-150 km.
Ensemble RNN–Random Forest Model for Earthquake Prediction Based onSpatio-Temporal Seismic Data Meyn Choudy Riovan Kaotel; Gladly Caren Rorimpandey; Sondy Campvid Kumajas; Muhammad Zulkifli
Edu Komputika Journal Vol. 12 No. 1 (2025): Edu Komputika Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukom.v12i1.35332

Abstract

This study proposes a hybrid Ensemble RNN–Random Forest (RNN–RF) model for short-term earthquake prediction based on spatio-temporal seismic data from the Sulawesi–Maluku region. The purpose of this research is to develop a lightweight and interpretable machine learning framework that integrates temporal and spatial features using local datasets provided by the Manado Geophysical Station of BMKG. The methodology includes six stages: data acquisition, preprocessing, feature engineering, model development, ensemble integration, and evaluation. The RNN captures sequential dependencies in seismic activity, while the Random Forest learns spatial and contextual relationships such as fault proximity and event clustering. The ensemble fuses probabilistic outputs (0.75 RNN and 0.25 RF) followed by domain-based calibration using mean magnitude, event frequency, and fault distance. Experimental results show that the proposed ensemble achieved F1 = 0.89 and AUC = 0.975, outperforming individual RNN and RF models in predictive stability and accuracy. The model demonstrates that integrating domain-specific adjustments enhances both recall and precision, while maintaining interpretability for operational deployment. This study contributes to explainable AI in seismology by bridging deep temporal modeling with geophysical reasoning, offering a scalable approach for early-warning applications in Indonesia.
Co-Authors Farly Reynol Tumimomor Ferdy Dungus Gladly Caren Rorimpandey Heinrich Taunaumang Meyn Choudy Riovan Kaotel Pamela Sifra Lumingas Sesar Prabu Dwi Sriyanto Sisca Cicilya Sabonbali Sondy Campvid Kumajas