Penerapan Algoritma K-Means Clustering Untuk Karakteristik Gempa Bumi Di Wilayah Sumatera Berdasarkan Magnitudo Dan Kedalaman

Salman Al Farisi, Farel Hansah Pradana, Alansyah Alansyah, Pujianto Pujianto

Abstract


Indonesia is one of the countries with high seismic activity due to its location at the convergence of several tectonic plates. This condition generates a large amount of earthquake data that requires proper analysis to obtain meaningful information. This study aims to cluster earthquake data in the Sumatra region using the K-Means algorithm based on magnitude and depth attributes. The dataset consists of 3,515 earthquake records obtained from an open data source. The research stages include data understanding, data preparation, clustering, evaluation using the Silhouette Score, and result visualization through Scatter Plot. The results show that the earthquake data can be grouped into three clusters with different characteristics based on magnitude and depth values. These differences provide a clearer understanding of earthquake variations in the Sumatra region. The clustering results are expected to support disaster-related data analysis and serve as a reference for future studies in the fields of data mining and earthquake analysis.

Keywords


Clustering, Data Mining, Earthquake, K-Means, Sumatra

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