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All Journal Jurnal Informatika Scientific Journal of Informatics JURNAL ILMIAH INFORMATIKA JURNAL TEKNOLOGI DAN OPEN SOURCE IJISTECH (International Journal Of Information System & Technology) JURIKOM (Jurnal Riset Komputer) Minda Baharu Building of Informatics, Technology and Science JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) IJISTECH Journal of Information System and Technology (JOINT) International Journal of Research in Vocational Studies (IJRVOCAS) JUPADAI : Jurnal Pengabdian Kepada Masyarakat Jurnal Siteba Jurnal Pengabdian Barelang Prosiding Seminar Nasional Ilmu Sosial dan Teknologi (SNISTEK)
Alvendo Wahyu Aranski
Institut Teknologi Batam, Indonesia
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Journal : Building of Informatics, Technology and Science

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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) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5366

Abstract

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
Co-Authors Abrar, Mohammad Masril Andi Supriadi Chan, Andi Supriadi Andri Yunaldi Ardiansyah, Luky Aulia Agung Dermawan Azkia, Muhammad Zukhruful Bagus Andhika Tuah Aprilyana Malaka Bilbina, Andhin Nabila Burhan, Rifatul Mahmudah candra, joni eka Darmansah, Darmansah Destiadi, Rezha Dimas Akmarul Putera Dwi Ely Kurniawan Dwi Maharani, Suci Ellbert Hutabri Franky Chia Handoko, Koko Hasibuan, Annalisa Sonaria Hasla, Salsabila Dwi Putri Hendri Kremer Indra Lesmana, Naufal januardi nasir Kremer, Hendri Lawi, Ansarullah Lesmana, Naufal Indra Mielanda, Ezzy Mohammad Jufri Muhammad Abrar Masril Muhammad Jufri Muhammad Jufri munir, Zainul Munir Nasution, Nadia Widari Nopriadi Nopriadi Noviardi, Refli Putra, Riko Andrian Rafi Dio Rahmat Fauzi Rahmat Fauzi Ramadona, Tri Suci Ririt Dwiputri Permatasari Rizki, Sestri Novia Sarah Astiti S.Kom., M.MT Sestri Novia Rizki Situmorang, Deslika Sugiyanto, Joko Sunarsono, Hery Wibisono, Muhammad Chairul Winata, Ardiya Yasha Langitta Setiawan Yunaldi, Andri