Implementation of the K-Means Algorithm for Clustering Zoning of Natural Disaster-Prone Areas in Pati Regency - Arifin Arifin; Bambang Agus Herlambang; Aris Tri Joko Harjanto (SISTEMASI, 2026)
Based on data from the Central Bureau of Statistics (BPS) of Pati Regency, during the 2018–2022 period the region frequently experienced natural disasters, particularly floods and landslides, across many areas.The high frequency of these disasters and the lack of proper mapping of affected areas present challenges that need to be addressed effectively. By utilizing technology and the K-Means clustering method, this study proposes an alternative solution to identify and map areas that are vulnerable to natural disasters. The results of the analysis indicate that Pati Regency can be divided into three clusters: Cluster 1 represents highly disaster-prone areas, accounting for 42.86% (9 regions); Cluster 2 represents disaster-prone areas, accounting for 19.04% (4 regions); and Cluster 3 represents areas with low disaster vulnerability, accounting for 38.1% (8 regions). The visualization results are presented as a classification map of regions based on their disaster vulnerability levels. This map can serve as a reference for local governments and relevant institutions in formulating more targeted and effective disaster mitigation policies.