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All Journal Jurnal Ilmu Komputer dan Agri-Informatika Jurnal CoreIT INOVTEK Polbeng - Seri Informatika Jurnal Informatika Universitas Pamulang JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science Zonasi: Jurnal Sistem Informasi JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Information System Journal (INFOS) Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Informatika: Jurnal Pengembangan IT
Fadhilah Syafria
UIN Suska Riau
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Journal : TIN: TERAPAN INFORMATIKA NUSANTARA

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Clustering Data Penduduk Menggunakan Algoritma K-Means Ikhsan, Tomi; Haerani, Elin; Wulandari, Fitri; Syafria, Fadhilah
TIN: Terapan Informatika Nusantara Vol 5 No 12 (2025): May 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i12.7328

Abstract

Economic inequality is still a crucial factor facing Indonesia today, from big cities to remote villages, economic inequality is still a major problem. Bina Baru Village is no exception, a village inhabited by 5,760 people with a total of 1,742 families, spread across 30 neighbourhood associations (RT) and 8 community associations (RW). Various efforts are made to overcome the problem of economic inequality, one of which is by channeling assistance or providing policies that are right on target. One of the steps to overcome this problem is to group population data in Bina Baru village using the K-Means Clustering method which aims to determine the economic level of families in the region, so that local governments can more accurately make policies on the problem of economic inequality that occurs. The data used comes from a questionnaire of 1,005 family data with 64 attributes and 1,005 individual data with 84 attributes. The application of the k-means algorithm is carried out using python, also using DBI (Davies-Bouldin Index) to determine the optimum k value. In this study, the optimal k value is 3 clusters. Based on testing, it is found that Cluster 0 represents households with medium economic conditions, cluster 1 represents groups with better economic conditions and Cluster 2 is a group of households with low economic conditions. By clustering the population's economy, it is hoped that it can help stakeholders to provide targeted policies.
Co-Authors Abdul Aziz Adzhima, Fauzan Afriyanti, Liza Agus Buono Agustina, Auliyah Aprima, Muhammad Dzaky Bib Paruhum Silalahi Che Hussin, Ab Razak Dede Fadillah Elin Haerani Elvia Budianita Faska, Ridho Mahardika Fauzan Adzim Febi Yanto, Febi Fitri Insani Fitri Wulandari Gusti, Siska Kurnia Guswanti, Widya Harni, Yulia Iis Afrianty Ikhsan, Tomi Israldi, Tino Iwan Iskandar Jasril Jasril Khair, Nada Tsawaabul Ma'rifah, Laila Alfi Masaugi, Fathan Fanrita Mazdavilaya, T Kaisyarendika Muhammad Fahri Muhammad Syafriandi, Muhammad Muhammad Yusuf Fadhillah Mulyono, Makmur Muslimin, Al’hadiid Nazir, Alwis Okfalisa Okfalisa Permata, Rizkiya Indah Pizaini Pizaini, Pizaini Putra, Fiqhri Mulianda Putri, Widya Maulida Rahmad Abdillah Rahmadhani, R. Ramadhan, Aweldri Ramadhani, Siti Siti Sri Rahayu Suwanto Sanjaya Syaputra, Muhammad Dwiky Zabihullah, Fayat Zulastri, Zulastri