<![CDATA[The Free Nutritious Meal Program requires an objective and standardized approach to evaluate the eligibility of food products supplied by Micro, Small, and Medium Enterprises (MSMEs). This study aims to develop a machine learning-based classification model using Random Forest to support the initial screening of MSME food product eligibility. A real-world dataset containing 120 MSME food product records was utilized, consisting of nutritional, economic, legality, certification, and packaging quality attributes. The data were preprocessed and divided into training and testing sets using an 80:20 ratio. The Random Forest model achieved the best classification performance, obtaining an accuracy of 96.00%, precision of 93.75%, recall of 100.00%, and F1-score of 96.77%. Feature analysis showed that PIRT license, packaging hygiene, and halal certification were the most influential factors in determining product eligibility. The proposed model provides practical support for improving the objectivity, efficiency, and documentation of MSME food product screening in the implementation of the Free Nutritious Meal Program. This study is limited by the use of a small-scale prototype dataset; therefore, future research should involve larger real-world datasets and further validation in operational environments.]]>
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