Prediction Analysis of Kartu Indonesia Pintar (KIP) Fund Recipients Using the Naïve Bayes Method - Amali Amali; Nurhadi Surojudin (Journal Innovation in Information and Computer Technology, 2025)
The Kartu Indonesia Pintar (KIP) program is a government initiative aimed at supporting students from economically disadvantaged families in obtaining proper educational opportunities. However, the distribution of KIP assistance is often considered inaccurate due to the complexity of determining eligible recipients based on multiple criteria. This study aims to analyze the implementation of the Naïve Bayes algorithm in predicting KIP recipients at Pelita Bangsa University. The research applies a data mining approach using several variables, including attendance, academic grades, parental dependents, housing conditions, and parents’ income. The dataset used in this study consists of 50 student data records that were processed through data selection and preprocessing stages before classification. The Naïve Bayes method was implemented and tested using the RapidMiner application to evaluate classification performance. The results show that the proposed model achieved an accuracy level of 96% with an error rate of 4%. In addition, evaluation using the Receiver Operating Characteristic (ROC) Curve produced an Area Under Curve (AUC) value of 0.979, indicating excellent classification capability. The findings demonstrate that the Naïve Bayes algorithm is effective and suitable for supporting objective decision-making in determining KIP assistance recipients based on predefined socioeconomic criteria.