<![CDATA[The distribution of subsidized rice in Indonesia faces persistent challenges such as mistargeting, inefficiency, and delays, which hinder food access among vulnerable populations. This study aims to address these issues by developing an intelligent decision support system that leverages Fuzzy Logic and Naïve Bayes classification to enhance the accuracy and efficiency of distribution decisions. Fuzzy Logic is utilized to process uncertain and imprecise data in decision criteria, while Naïve Bayes is employed to analyze historical data and predict recipient eligibility based on socioeconomic indicators such as income level, number of dependents, and asset ownership. The system demonstrates high classification accuracy and generates recommendations that align well with real-world conditions. The integration of these two methods effectively simplifies complex decision-making processes in social aid distribution. In conclusion, the proposed system offers a robust and objective tool to support the fair and transparent allocation of rice subsidies. Clinically or practically, this work has the potential to be adopted by government agencies to improve policy implementation in food assistance programs, ensuring more equitable and data-driven outcomes.]]>
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