Data-Driven Prioritization of Productive Zakat Distribution: Evidence from Poverty and Social Assistance Gaps in East Java

Prioritisasi Penyaluran Zakat Produktif Berbasis Data: Bukti dari Kesenjangan Kemiskinan dan Bantuan Sosial di Jawa Timur

Abstract

This study develops a priority model for productive zakat distribution by aligning poverty data with gaps in government assistance in East Java, Indonesia. Using a quantitative evaluative approach, we analyzed secondary data from 38 districts/municipalities, focusing on poverty rates and the number of food aid beneficiaries. A composite scoring model was constructed, assigning higher weight to poverty (60%) and lower weight to social aid coverage (40%) to identify underserved regions. Findings show a significant mismatch between poverty levels and government assistance allocation, with areas like Sampang, Bangkalan, and Sumenep having high poverty but limited aid coverage. This highlights the urgency of data-driven zakat targeting to address distribution inefficiencies. The proposed scoring model ranks districts transparently, offering a replicable tool for local zakat institutions. Integrating public data sources—such as BAZNAS reports and BPS statistics—into zakat planning can enhance precision, transparency, and social justice. The study contributes methodologically to Islamic social finance and offers practical policy implications to support national zakat governance and Sustainable Development Goals (SDGs). Future research is encouraged to enrich the model by incorporating multidimensional indicators and validating outcomes in broader contexts