Komparasi Klasterisasi Data Historis Gempa Bumi Menggunakan DBSCAN, K-Means, dan Agglomerative Clustering
- Eka Therina Lakeisyah Universitas Sriwijaya, Palembang, Indonesia
-
Ken Ditha Tania
*
Universitas Sriwijaya, Palembang,
Indonesia
- Mira Afrina Universitas Sriwijaya, Palembang, Indonesia (*) Corresponding Author
Abstract
Earthquakes are one of the natural disasters that are prone to occur on the island of Sumatera and pose a serious challenge because they can have a devastating impact on human life, such as loss of life, material losses, and environmental damage. Therefore, earthquake hazard zone mapping is needed to provide information about the potential and history of disasters and is an important tool for disaster mitigation efforts. This study aims to map earthquake vulnerability in Sumatra by comparing three clustering algorithms, namely DBSCAN, K-Means, and Agglomerative Clustering, based on earthquake data in Sumatra from 1973 to 2023. This is to find the best algorithm so that it can provide recommendations for appropriate earthquake risk mitigation strategies. The results show that the K-Means algorithm is the best because it obtained the highest Silhouette Coefficient value, namely 0.3948 with a total of 3 clusters. It is hoped that this research can improve understanding of earthquake hazard zones on the island of Sumatra and provide practical contributions in the form of mitigation strategy recommendations tailored to the characteristics of each cluster to support the application of this research for the government and local communities.
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