<![CDATA[Manual determination of eligibility for Prospective Farmers and Prospective Locations (CPCL) assistance in swamp rice farming often leads to subjectivity, time-consuming processes, and misallocated aid. This study aims to construct a classification model to evaluate the eligibility of CPCL swamp rice assistance recipients in Kubu Raya Regency for 2024 using the Random Forest algorithm. The CPCL dataset underwent a comprehensive preprocessing pipeline, including data cleaning, missing value handling, categorical variable encoding, and feature selection. The model was trained using an 80:20 data split, tested on 26 test samples (7 "Ineligible" and 19 "Eligible"), and evaluated using Confusion Matrix metrics and Stratified 10-Fold Cross-Validation. The results demonstrate that the Random Forest model achieved an accuracy of 84.62% with a weighted average of 87% for precision, 85% for recall, and 82% for F1-score. For the "Eligible" class, the model achieved a recall of 1.00, precision of 0.83, and F1-score of 0.90. Meanwhile, for the "Ineligible" class, the model attained a precision of 1.00, recall of 0.43, and F1-score of 0.60. Feature importance analysis revealed that seed requirements, fertilizer allocation, pesticide supply, and land area were the primary factors influencing prediction outcomes.]]>
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