Clustering Indonesian Provinces by Disaster Intensity using K-Means Algorithm: a Data Mining Approach

Indonesia, as part of the Pacific Ring of Fire, is highly susceptible to various natural hazard events including geological and hydrometeorological disasters. This study aims to classify Indonesian provinces based on their disaster vulnerability using the K-Means clustering algorithm. The clustering process considers area and population data to ensure accurate grouping. The results of the study successfully categorized Indonesian provinces into distinct clusters based on their disaster vulnerability levels. These clusters provide valuable insights for the National Disaster Management Agency (BNPB) and local governments in prioritizing disaster preparedness and response efforts. By identifying regions with higher vulnerability, this research contributes to the development of more targeted and effective disaster mitigation strategies, ultimately helping to reduce potential loss of life and property.