Clustering Indonesian
Provinces by Disaster Intensity using K-Means Algorithm: a Data Mining Approach
Wibowo A., Rohman N., Rusdah, Achadi A.H. and Amri I.
Disaster Advances; Vol. 17(12); 1-8;
doi: https://doi.org/10.25303/1712da0108; (2024)
Abstract
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.
