Pemanfaatan K-Means Clustering Untuk Pengelompokan Dan Pemetaan Bencana Alam Di Indonesia - Otniel, Marcelinus Vito; Prasetyo, Sri Yulianto Joko (Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen), 2024)
Indonesia's geographical and geological conditions, which are prone to natural disasters, necessitate the country to mitigate their impact by identifying causes and studying previous disaster events through existing disaster data analysis. This study aims to map cities or regencies in Indonesia based on the clustering results using the K-Means clustering algorithm in the R programming language. Disaster data management, from collection to dissemination, plays a crucial role in disaster management. The research findings reveal that natural disaster data from 2019-2021 divided cities or regencies in Indonesia into five clusters, with Java Island identified as the most vulnerable region to natural disasters compared to other regions. Cluster visualization is presented in the form of a map to facilitate quick reading and understanding of information