<![CDATA[The accurate targeting of social assistance recipients is a critical challenge in public policy implementation, particularly in efforts to reduce poverty and social inequality. This study aims to apply the Decision Tree C4.5 algorithm to determine the eligibility of social assistance beneficiaries based on socio-economic data. The research employs a quantitative approach using data mining techniques, where data preprocessing, model construction, and performance evaluation are conducted systematically. The C4.5 algorithm is selected due to its ability to handle numerical and categorical data and to produce interpretable decision rules. The results indicate that the proposed model achieves a high classification performance, with income level emerging as the most influential attribute, followed by household dependents and housing conditions. The generated decision tree provides clear and transparent rules that facilitate understanding of eligibility determination. These findings demonstrate that the C4.5 algorithm is effective not only in terms of accuracy but also in supporting explainable decision-making processes. The study concludes that integrating Decision Tree C4.5 into social assistance management can enhance objectivity, transparency, and policy effectiveness. This research contributes to the development of data-driven decision support systems in the public sector and offers practical insights for improving the accuracy of social assistance distribution.]]>
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