Pengelompokkan Daerah Rawan Bencana di Indonesia Menggunakan Metode Clustering K-Means

Abstract

Listen

Translate article

Indonesia is a country with a high level of disaster vulnerability, influenced by tectonic activity and its tropical climate. This study uses the K-Means clustering method to identify and group disaster-prone areas based on the level of vulnerability. The data used included average temperature (Tavg) and rainfall (RR) which were processed using Python. The analysis process includes data collection, pre-processing, determination of key features, and evaluation of clustering quality using the Elbow and Silhouette Score methods. The results of the grouping show two main patterns, namely flood-prone areas and drought-prone areas. These findings are expected to support the government in more effective and data-based disaster mitigation planning.