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All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JSAI (Journal Scientific and Applied Informatics) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA Journal of Computer Networks, Architecture and High Performance Computing Journal of System and Computer Engineering Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Journal Automation Computer Information System (JACIS) Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen
Prabowo, Tito
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Journal : TIN: TERAPAN INFORMATIKA NUSANTARA

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Optimasi Akurasi Model Prediksi Magnitudo Gempa Bumi dengan Integrasi Clustering DBSCAN pada Ensemble Learning (Random Forest & XGBoost) Syaifuddin, Akhmad; Prabowo, Tito
TIN: Terapan Informatika Nusantara Vol 5 No 7 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i7.6522

Abstract

Earthquake prediction is crucial for risk mitigation, particularly in taking appropriate preventive measures in the face of disasters. The magnitude of an earthquake is influenced by various factors, including location, depth, and the history of seismic activity in a region. This study aims to develop an accurate earthquake magnitude prediction model by integrating clustering and ensemble learning techniques. Earthquake catalog data from BMKG Indonesia is processed and clustered using the DBSCAN algorithm based on geographical location. The prediction model is constructed using Random Forest and XGBoost, then integrated through stacking ensemble learning techniques. Evaluation results indicate that the stacking model delivers the best performance, with the lowest Mean Squared Error (MSE) of 0.108 and the highest R-squared (R²) of 0.892, compared to individual models. This accuracy improvement is attributed to stacking’s ability to combine the predictive strengths of Random Forest and XGBoost. The study demonstrates that integrating clustering and ensemble learning can enhance earthquake magnitude prediction models. However, further research is needed to explore more comprehensive data and features and to test model generalization in other regions.
Co-Authors Abd. Charis Fauzan Baqi, Utrodus Said Al Dewi Lestari FATRA NONGGALA PUTRA Harliana Harliana Harliana Harliana Huda, Muhamat Maariful Huda, Muhmat Maariful Kartika Dewi Khasanah, Walidatul Isna Lestariningsih Machfud, Imam Mafula, Veradella Yuelisa Maharani, Ayu Pramesti Muhammad Rizky Ramadhan Munir, M.Sirojul Nuha, M. Ulin Nuraeni, Septiya Rouf, M. Abd. Rusdian Yusron, Rizki Darma Syaifuddin, Akhmad Yuniar Alam, Yuniar Yusron, RDR