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All Journal Jurnal Stasiun Geofisika Sleman (JSGS) Jurnal Meteorologi dan Geofisika
Setyoajie Prayoedhie
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Earth & Planetary Sciences Energy Physics

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Analisis Koefisien Korelasi Peak Ground Acceleration Dengan Metode Oliviera dan Shakemap BMKG, Studi Kasus Gempabumi Bantul 16 Januari 2022 Salma Aprillina Kurniawan; Efita Pratiwi Adi; Wuri Handayani; Setyoajie Prayoedhie
Jurnal Stasiun Geofisika Sleman Vol. 1 No. 1 (2023): Jurnal Stasiun Geofisika Sleman (JSGS)
Publisher : Stasiun Geofisika Kelas I Sleman

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Abstract

The analysis of the coefficient correlation of peak ground acceleration (PGA) using Oliviera Method and BMKG’s shakemap based on case study of the earthquake in Bantul January, 16th 2022. This study aims to determine of peak ground acceleration value and to determine coefficient correlation value of peak ground acceleration and BMKG’s shakemap. The earthquake data used is the earthquake data on January 16th 2022 in Bantul. Based on this data, PGA value when using the Oliviera method is 10.109 gals or 0.0101 g. Meanwhile, PGA value of BMKG’s shakemap is 70.28543 gals or 0.070285 g. The result of the coefficient correlation between PGA value uses Oliviera method with PGA value of BMKG’s shakemap is 0.9958. It shows the relationship between the two values has a positive curve and shows a strong relationship.
Machine Learning-based Ground Motion Prediction Models for Indonesia: A Performance Evaluation of XGBoost and Random Forest Against Conventional GMPE Rayhan Irfan Hielmy; Edy Santoso; Muzli Muzli; Sigit Pramono; Setyoajie Prayoedhie
Jurnal Meteorologi dan Geofisika Vol. 27 No. 1 (2026)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v27i1.1233

Abstract

Accurate ground motion prediction is critical for seismic hazard mitigation in Indonesia, a region characterized by complex tectonic settings. Conventional Ground Motion Prediction Equations (GMPEs) often struggle to capture the non-linear attenuation characteristics of local seismicity. This study develops national-scale Machine Learning (ML) models—specifically XGBoost and Random Forest (RF)—to predict Peak Ground Acceleration (PGA) and benchmarks them against the widely used Zhao et al. [23] model. The dataset comprises 20,287 strong-motion records from 1,573 events (M 1.6–7.9) recorded by 667 stations across Indonesia between January 2023 and April 2025. Performance evaluation reveals that the XGBoost model outperforms both RF and the conventional GMPE, achieving the lowest Mean Squared Error (MSE) of 0.8885. In contrast, the conventional model showed less reliability with a significantly higher MSE of 1.5905. Feature importance analysis indicates that hypocentral distance and magnitude are the dominant predictors, while site condition (Vs30) plays a secondary role. Validation on independent test sets and recent significant seismic events confirms the model's robust generalization capability compared to conventional approaches. These findings demonstrate that ML-based approaches provide a more reliable alternative for estimating ground motion in Indonesia, offering significant potential for improving early warning systems and hazard maps.
Co-Authors Edy Santoso Efita Pratiwi Adi Muzli Muzli Rayhan Irfan Hielmy Salma Aprillina Kurniawan Sigit Pramono Wuri Handayani