<![CDATA[The implementation of the Cash Transfer Assistance (BLT) program in Indonesia aims to assist economically disadvantaged communities, especially during crises such as the pandemic. However, the selection process for BLT recipients often faces challenges related to accuracy and efficiency, particularly in determining eligible recipients based on various economic and social criteria. This research develops an information system based on the K-Nearest Neighbor (K-NN) method to address these issues. The system is designed to classify BLT candidates by considering several variables, such as family income, number of dependents, employment status, housing conditions, and family health. The optimal K value was determined through trial and error to achieve the highest accuracy. The system was tested using both training and testing data, and the evaluation results showed an accuracy rate of 85%. This information system not only processes data quickly but also provides transparent and objective results, making it useful for village authorities to efficiently select BLT recipients. By implementing the K-NN algorithm, this system is expected to offer a practical solution for village governments in improving the accuracy of aid distribution to eligible communities.]]>
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