Pengaplikasian Data Mining Dalam Mengelompokan Data Penerima Bantuan Subsidi Rumah dengan Menggunakan Metode K-Means Clustering - Aranski, Alvendo Wahyu; Astiti, Sarah; Putra, Riko Andrian; Darmansah, Darmansah (Building of Informatics, Technology and Science (BITS), 2024)
From 2015 until now, the government has provided assistance for house renovations in the Tembesi area, Sagulung sub-district, Batam City. However, in determining the provision of assistance, sub-district governments sometimes face problems in determining which people will receive housing subsidies and there is no scheme or category for determining recipients of assistance. Therefore, the author will conduct this research by grouping or clustering the eligibility of recipients of housing subsidy assistance using the K-Means algorithm. The K-Means clustering algorithm can group each data into sets, so that data sets with the same characteristics will be grouped in the same set, or data sets with different characteristics will be grouped in different sets. The purpose of grouping is to determine that group 0 and group 1 are eligible to receive housing subsidy assistance, and group 1 is not. This research uses metrics such as number of family members, employment, housing conditions, and income. The results of this research obtained 91 data in cluster 0 and 79 data in cluster 1. Thus, from the 170 data, 91 people were eligible for housing subsidy assistance, and 79 people were not eligible