<![CDATA[Social assistance (BANSOS) is one of the government’s programs aimed at improving community welfare; however, its distribution often suffers from inaccuracy due to the absence of an objective system for determining eligible recipients. This study aims to develop a Decision Support System (DSS) using the Additive Ratio Assessment (ARAS) method to assess the eligibility of BANSOS recipients and determine the most appropriate type of assistance, such as the Family Hope Program (PKH), Indonesia Smart Program (PIP), and Non-Cash Food Assistance (BPNT). The dataset consists of 60 alternatives evaluated using 11 assessment criteria. The analysis results show that alternative Ar45 (Tumi) achieved the highest score of 0.7509, while most actual BANSOS recipients had relatively lower scores. The system’s accuracy rate of 33.33% indicates that the results do not yet fully represent real conditions. However, sensitivity testing shows that the ARAS method is relatively stable, with average value changes below 5% across variations in criterion weights. This finding confirms that ARAS provides objective, consistent, and transparent assessments. This study contributes by demonstrating the effectiveness of the ARAS method for decision support in determining social assistance eligibility at the village level. Its limitation lies in the small sample size and lack of testing in other regions. Future research is recommended to apply the system to larger datasets, compare it with other MCDM methods, and develop an Android-based application for easier access by policymakers.]]>
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