Optimizing Zakat Distribution with GIS and Data Mining in Community Empowerment at BAZNAS Deli Serdang

This research aims to develop an information system that utilizes Geographic Information System technology to map and analyze the distribution of zakat effectively in Deli Serdang Regency. The methods used include collecting data on zakat recipients and applying the K-means grouping method to identify distribution patterns in 22 districts. The results of the study show that there are three groups of zakat recipients: High Recipients, Medium Recipients, and Low Recipients. Tanjung Morawa was identified as the district with the highest number of zakat recipients, namely 239 people, which shows a significant need. The K-Means algorithm plays an important role in identifying areas that need help, so this Geographic Information System-based grouping is proven to improve the efficiency of zakat resource allocation. This allows BAZNAS to target assistance more precisely and strategically. This study recommends the use of this approach to optimize zakat management and support community economic empowerment, as well as reduce social inequality in the region.

Authors

Institutions

Publication Details

Journal
Journal of Information Systems and Informatics
Published
2024-12-31
DOI
https://doi.org/10.51519/journalisi.v6i4.942
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Optimizing Zakat Distribution with GIS and Data Mining in Community Empowerment at BAZNAS Deli Serdang

Triase Triase, Sinta Dara Efita
Journal of Information Systems and Informatics
Data Mining and Machine Learning Applications
article

Optimizing Zakat Distribution with GIS and Data Mining in Community Empowerment at BAZNAS Deli Serdang

Triase Triase, Sinta Dara Efita
article en

Abstract

This research aims to develop an information system that utilizes Geographic Information System technology to map and analyze the distribution of zakat effectively in Deli Serdang Regency. The methods used include collecting data on zakat recipients and applying the K-means grouping method to identify distribution patterns in 22 districts. The results of the study show that there are three groups of zakat recipients: High Recipients, Medium Recipients, and Low Recipients. Tanjung Morawa was identified as the district with the highest number of zakat recipients, namely 239 people, which shows a significant need. The K-Means algorithm plays an important role in identifying areas that need help, so this Geographic Information System-based grouping is proven to improve the efficiency of zakat resource allocation. This allows BAZNAS to target assistance more precisely and strategically. This study recommends the use of this approach to optimize zakat management and support community economic empowerment, as well as reduce social inequality in the region.

Journal of Information Systems and InformaticsVol. 6(4)
Universitas Islam Negeri Sumatera Utara (ID)
Openalex Percentile: Top 55%
Data Mining and Machine Learning Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.