<![CDATA[The Indonesia Pintar–Kuliah card (KIP-K) program is a government-funded educational assistance initiative aimed at supporting financially disadvantaged students. The selection process requires accurate data analysis to ensure that the assistance is distributed appropriately. This study aims to develop a classification model for predicting KIP-K recipients using the Naive Bayes algorithm based on several attributes, including family income, number of dependents, housing condition, parents’ occupation, social assistance status, GPA, attendance, and income per capita. A dataset of 200 student records was preprocessed and encoded before the model was trained using an 80:20 train–test split. The model’s performance was evaluated through accuracy, precision, recall, and F1-score metrics. The results indicate that the Naive Bayes algorithm achieves satisfactory classification performance, with an accuracy score of (insert your model accuracy). These findings highlight the potential of machine learning techniques to support a more objective and efficient selection process for KIP-K recipients.]]>
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