Clustering and Risk Analysis of The Earthquake in Sulawesi Using Mini Batch K-Means, K-Medoids, and Maximum Likelihood Method
Abstract
Abstract: The earthquake events have been widely analyzed using a statistical approach. Therefore, the sole purpose of this research is clustering and risk analysis of earthquake events based on the combination of machine learning and statistics. The machine learning, conducted by Mini Batch K-Means and K-Medoids, is validated by the Davies-Bouldin index method to earthquake events cluster. Furthermore, the statistics approach conducted by the maximum likelihood method is to estimate the b-value and a-value of earthquake events. The data used in the earthquake events analysis in Sulawesi have a magnitude 5 SR during the period 1980-2022. The results show that the Mini Batch K-Means method is more efficient and accurate than the K-Medoids, and can cluster the earthquakes, namely cluster 0 below 100 km (shallow earthquake), cluster 1 above 100 km to 350 km (medium earthquake), cluster 2 above 350 km (deep earthquake), while K-Medoids method has two clusters namely cluster 0 below 100 km (shallow earthquake), and cluster 1 above 100 km to 350 km. The regions with b-value and a-value less than 0.9 and 7.5, respectively, and in cluster 0, namely the western part of North Sulawesi, Gorontalo, Middle Sulawesi, and West Sulawesi Province, are as vulnerable to earthquake disasters. Meanwhile, the region in cluster 1 and cluster 2 with b-value and a-value more than 0.9 and 7.5 respectively namely South Sulawesi, the Northern part of North Sulawesi, and Southeast Sulawesi Province, are categorized as minor earthquake disasters. Furthermore, the clustering and risk analysis based on these methods results are good performance, which has recognised cluster and vulnerability of the earthquake events.
Abstrak: Kejadian gempa bumi telah banyak dianalisis dengan menggunakan pendekatan statistik. Oleh karena itu, tujuan penelitian untuk menganalisis kejadian gempa dengan menggunakan kombinasi pendekatan machine learning dengan statistik. Pendekatan machine learning dilakukan dengan metode baru yakni metode Mini Batch K-Means dan K-Medoids yang divalidasi dengan metode Davies-Bouldin indeks yang digunakan untuk mengklaster kejadian gempa, sedangkan pendekatan secara statistik dilakukan dengan metode maximum likelihood untuk mengestimasi kerentanan gempa bumi berdasarkan nilai-b dan nilai-a. Data yang digunakan yakni data kejadian gempa di Sulawesi dengan magnitudo ≥ 5 SR dengan periode 1980-2022. Hasil menunjukan bahwa metode Mini Batch K-Means lebih effisien dan akurat dibandingkan dengan metode K-Medoids, dan mengklasifikasi tiga klaster kedalaman gempa, yakni klaster 0 dengan kedalaman kurang dari 100 km (gempa dangkal), klaster 1 dengan kedalaman diantara 100 km dengan 350 km (gempa menengah), klaster 2 dengan kedalaman lebih dari 350 km (gempa dalam). Sementara metode K-Medoids dua klaster kedalama gempa, yakni klaster 0 dengan kedalaman dibawah 100 km (gempa dangkal), dan klaster 1 dengan kedalaman lebih dari 100 km. Beberapa wilayah yang mempunyai nilai-b dan nilai-a secara berurutan kurang dari 0,9 dan 7,5 dan termasuk ke dalam klaster 0, yakni Provinsi Sulawesi Utara bagian barat, Gorontalo, Sulawesi Tengah, dan Sulawesi Barat dikategorikan rawan terhadap bencana gempa; Sedangkan wilayah yang termasuk ke dalam klaster 1 dan klaster 2 dengan nilai-b dan nilai-a secara berurutan lebih dari 0,9 dan 7,5 yakni Provinsi Sulawesi Selatan, Sulawesi Utara bagian Utara, dan Sulawesi Tenggara dikategorikan sebagai rendah terhadap bencana gempa. Dengan demikian, kedua metode dapat digunakan untuk meng-klaster gempa dan identifikasi kerentanan kejadian gempa bumi.
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DOI: http://dx.doi.org/10.22373/ekw.v8i2.13027
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