The Use of K-Means Clustering Algorithm to Determine Potential Region for Zakat Distribution Based on Social and Economic Data in Indonesia 2023
Main Article Content
Abstract
Poverty remains a significant challenge in many developing nations, including Indonesia. Poverty is also considered a problem caused by uneven economic growth. An effective approach to reducing poverty is through the equitable distribution of zakat, which can help narrow the disparity between the wealthy and those in need. Equitable distribution of zakat can be done by determining the level and potential of poverty in each province in Indonesia. To find out the poverty potential in each province in Indonesia, it is necessary to conduct a cluster analysis by looking at several poverty and economic growth variables. Cluster analysis is an analytical method employed to classify similar objects or individuals based on multiple criteria. Cluster analysis specifically used is k-means clustering which divides provinces in Indonesia into groups by identifying any similarities in the economic growth variables of each province. This analysis aims to categorize provinces in Indonesia based on economic conditions and levels, thereby assisting the government and zakat institutions in the effective distribution of zakat.
Downloads
Article Details

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
BAZNAS. (2023, 10 2). Get to know 8 Asnaf Zakat. Retrieved from Badan Amil Zakat Website: https://baznas.go.id/artikel-show/Mengenal-8-Asnaf-Zakat.
BAZNAS, P. K. (2024). Outlook Zakat 2024. Jakarta: Pusat Kajian Strategis BAZNAS.
Benbrahim, S., Chaoueche, N. S., & Toumache, R. (2022). Countries Economic Segmentation using K-Means Clustering for the Year 2021. Economics Researcher Journal Vol. 9, 512-528.
Buslim, N., Iswara, R. P., & Agustian, F. (2021). The Modeling of Mustahiq Data Using K-Means Clustering Algorithm and Big Data Analysis (Case Study: LAZ). Jurnal Teknik Informatika, 213-230.
Gudono. (2014). Analisis Data Multivarit Edisi Tiga. Yogyakarta: BPFE Yogyakarta.
Hadi, M. (2010). Problematika Zakat Profesi dan Solusinya. Yogyakarta: Pustaka Pelajar.
Han, J., Kamber, M., & Pei, J. (2012). Data Mining Concepts and Techniques 3rd Edition. Massachusetts: Morgan Kaufmann.
Harahap, S. S. (2006). Critical Analysis of Financial. Jakarta: PT. Raja Grafindo Perkasa.
James, G. (2019). Introductions to Macroeconomics. Colorado: Gilad James Mystery School.
Johnson, R. A., & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis (6th Ed). New Jersey: Prentice International Inc.
Kartikasari, E. (2006). Introduction to Zakat and Waqf. Jakarta: PT Grasindo.
Monica, I. S., & Abidah, A. (2021). Konsep Asnaf Berdasarkan Pemikiran Yusuf Al-Qardawi dan Al-Zuhayli. Jurnal Antologi Hukum, 109-124.
Mufraini, A. (2005). Zakat Management and Accountant. Jakarta: Pernada Media.
Purba, B. (2020). Analisis Pertumbuhan Ekonomi Indonesia Periode 2009 - 2018. Jurnal Humaniora Vol. 4, 244-255.
Rachmatin, D. (2014). Aplikasi Metode-Metode Agglomerative dalam Analisis Klaster pada data TIngkat Polusi Udara. Jurnal Ilmiah Studi Matematika STKIP Siliwangi Bandung, 133-149.
Sari, R. M. (2024). Clustering Method for Dangerous Disease. Sumatera Barat: PT Serasi Media Teknologi.
Statistik, B. P. (2023, July 17). BPS: Berita Resmi Statistik. Retrieved from Profil Kemiskinan di Indonesia Maret 2023: https://www.bps.go.id/id/pressrelease/2023/07/17/2016/profil-kemiskinan-di-indonesia-maret-2023.html
Turnando, G., & Zein, A. S. (2019). Analisis Pengaruh Zakat Terhadap Peningkatan Kesejahteraan Mustahiq. Al-Masharif: Jurnal Ilmu Ekonomi dan Keislaman, 162-175.