Pengelompokan Penerima Bantuan Kerusakan Bangunan Akibat Bencana Alam di Jawa Barat Menggunakan Algoritma K-Means - Rizky Maulana Arrasyid; Herlawati Herlawati (Journal of Students‘ Research in Computer Science, 2024)
Disasters have a tremendous impact on society, one of the impacts of natural disasters is building damage, a province that is very prone to natural disasters is West Java province and results in a lot of building damage due to disasters, the solution to this disaster needs building assistance caused by natural disasters. In this study discusses the application of the K-Means Clustering algorithm for recipients of aid due to natural disasters, this study took data from West Java open data with a data set of house damage this data consists of 2012-2022 covering 27 districts / cities in West Java Province. This research uses the Cross-Industry Standard Process for Data Mining (CRISP-DM) method which has six stages. The results of the data processed using K-Means clustering are divided into 4 clusters, namely, the level of highly prioritized clusters (C0), the level of prioritized clusters (C1), the level of less prioritized clusters (C2), and the level of non-prioritized clusters (C3), In this study, clusters that are highly prioritized in receiving assistance are Bogor Regency, Bandung Regency, Cianjur Regency, Garut Regency, Sukabumi Regency, and Tasikmalaya Regency.