The objective of Smart Indonesia Program (Program Indonesia Pintar: PIP) is to help school-aged people from poor / vulnerable / priority families to continue to receive education services to graduate from secondary education, both through formal and non-formal education channels. In its implementation, there are still many fraudulent in the proces of nominating proposal PIP funds and there are still many prospective students who should not receive PIP because they do not meet the technical guidelines provided by the Ministry of Education and Culture to determine the eligibility of prospective recipients of PIP funds can be done by schools and stakeholders, one of them by using classification techniques. One algorithm that is widely used in classification is the Naive Bayes Classifier (NBC) algorithm. In this study three data sharing techniques were used, namely Hold Out 70% training data and 30% testing data, K-Means Clustering, and also 10 Fold Cross Validation. Determination of the best data sharing technique will be determined by looking at the value of Accuracy, Precision, and Recall and also the value of Area Under Curve (AUC) which is illustrated by the Receiver Operating Characteristic (ROC) curve so that the NBC algorithm is generated with 10 Fold Cross Validation has a very good classification level with the values of accuracy, precision, and recall respectively at 97.40%; 100%; and 76.14%.

 Abstract: Presidential Instruction No. 7 of 2014 mandates PIP to the Ministry of Education and Culture to summarize Indonesia Smart Card (KIP) and spread PIP funds to students that cannot afford to pay education. However, Indonesia Corruption Watch (2018) explained that the data used for the Smart Indonesia Program (PIP) was still inaccurate because almost half of the poor people with a percentage of 42.9% were not registered as participants in the Smart Indonesia Program (PIP). According to ICW, this is due to the data used for the process of determining the candidates for the Smart Indonesia Program recipients of the funds are still inaccurate and harming others who supposed to get funds. One method that usually used as a decision-making technique in the research is the Multi-Objective Optimization Ratio Analysis (MOORA) method which is a multi-criteria decision-making that has five main steps as a technique and it can be used to rank prospective PIP fund recipients based on the highest to the lowest preference values. The results of this studyindicate that the first rank with the highest value was 0.0539 and the last rank with the lowest value was 0.0211 so it used to ease the stakeholders to determine the amount of KIP recipients based on the preference values. This method can be applied for stakeholders needed in compared to monotonous data processing using estimates.