Machine Learning-Based Clustering for Optimizing Zakat, Infaq, and Sadaqah Fund Distribution Among Ahmad Dahlan University Students

Abstract

Objective: This study aims to identify latent patterns in the distribution of zakat, infaq, and sadaqah funds among student beneficiaries at Universitas Ahmad Dahlan by grouping recipients according to their socio-economic, academic, and social characteristics. Method: This study adopts a quantitative approach using unsupervised machine learning techniques. The K-Means and Density-Based Spatial Clustering of Applications with Noise algorithms were applied to secondary data from Lazismu Ahmad Dahlan University for 2023–2026. After cleaning, the study retained 185 valid recipient records from an initial dataset of 194 observations. Data preprocessing included encoding and normalization. The study compares centroid-based (K-Means) and Density-Based Spatial Clustering of Applications with Noise clustering methods to identify recipient segments. Results: The findings show that Density-Based Spatial Clustering of Applications with Noise produces more compact and better-separated clusters than K-Means. The algorithm also identifies atypical recipients as noise, revealing unique beneficiary profiles. The results indicate substantial heterogeneity among recipients. Some economically vulnerable students demonstrate strong academic performance yet receive relatively lower assistance, while other groups receive higher levels of support despite less vulnerable conditions. Implications: The findings suggest that clustering analysis can support more objective, transparent, and adaptive ZIS fund distribution. Integrating such analysis into planning, implementation, and evaluation processes may improve targeting accuracy and promote equitable, evidence-based decision-making while still considering individual circumstances.