Optimasi Seleksi Penerima Bantuan PIP di SD Negeri 017107 Kisaran Naga dengan Metode Naïve Bayes
Keywords:
Naïve Bayes, Indonesia Pintar Program, Data Mining, Classification, CRISP-DMAbstract
This research aims to apply the Naïve Bayes method to determine the eligibility of receiving the Smart Indonesia Program (PIP) at the 017107 Kisaran Naga State Elementary School by analyzing 207 student data. The CRISP-DM approach was used through six stages: business understanding, data understanding, data preparation, modeling, evaluation, and implementation. The variables analyzed included means of transportation, KPS and KIP recipients, worth a pip, reasons for eligibility, number of siblings, distance from home to school, and parents' income.
The results showed that this method achieved 89% accuracy, 85% precision for the positive class, and 92% for the negative class. A total of 125 students (59.9%) were declared eligible to receive assistance, while 82 students (40.1%) did not meet the criteria. The Naïve Bayes method is effective in supporting decision-making for the provision of targeted educational assistance
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