<![CDATA[PIP is the provision of educational cash assistance to school-aged children from underprivileged families who are marked with a smart Indonesia card (KIP). The purpose of this research is to determine the performance of the Naïve Bayes method in classifying data on students who are eligible and who are not eligible to receive a PIP scholarship at SMAN 1 Sukamulia, because this school is still experiencing problems in the decision-making process for determining potential PIP scholarship recipients, because there are no a system that can assist in processing student data that is eligible and not eligible to get the PIP scholarship. Therefore, for data processing, researchers tried to implement a new system with the Data Mining concept using the Naïve Bayes method, by carrying out 9 tests using Cross Validation starting from K-Fold Validation 2 to 10, obtaining the highest accuracy results in the 9th test. using K-Fold Validation 10 which is equal to 92.81%. Also obtained was an Area Under Curve (AUC) value of 0.973%, where AUC is a parameter used in classification analysis to determine the best model for predicting a class or attribute. AUC itself has a value range of 0-1, which means that the closer the AUC value is to 1, the better the prediction or diagnosis of the attribute]]>
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