Penerapan Algoritma Clustering K-Means Untuk Menentukan Prioritas Penerima Bantuan Rumah Akibat Bencana Alam
Abstract
Disasters have a tremendous impact on society, one of the impacts of natural disasters is damage to houses, West Java is one of the provinces that is prone to disasters, and there is a lot of damage to houses due to disasters, it is necessary to provide housing assistance caused by natural disasters. In this study discusses the application of the K-Means Clustering algorithm for beneficiaries due to natural disasters, this study takes data from open data from West Java with the data set of house damage, the data used is data in 2012-2021 which consists of 27 regencies/cities that in West Java Province. This research uses the CRISPDM (Cross-Industry Standard Process for Data Mining) research method which consists of Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. The results of the data processed using K-Means clustering are divided into 4 clusters, namely, the very priority cluster level (C0), the priority cluster level (C1), the less priority cluster level (C2), and the non-priority cluster level (C3). These clusters that are very prioritized in receiving aid are Bekasi City, Subang Regency, Karawang Regency, and Indramayu Regency
References
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