Author: Mustakim, Siti Syahidatul Helma, Ulya Ramadhani, Achmad Daengs GS, Rice Novita, Nuryanti, Sri Rahmawati Fitriatien

Publish: 2nd International Conference on Advance & Scientific Innovation

Abstract:

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%.

Conclusion:

Based on the results of the study conducted using the Naive Bayes Classifier Algorithm, it can be accomplished that this algorithm can be used to classify the eligibility of prospective recipients of the Smart Indonesia Program using the 10-Fold Cross Validation data sharing technique because it has a higher value of accuracy, precision and recall if compared to other data sharing techniques that are Hold out and K-Means Clustering techniques. As well as having an AUC value described through the ROC curve of 0.937 or in other words the Naive Bayes Classifier classification algorithm using a 10 Fold Cross Validation data sharing technique can be categorized as Very Good / Excellent and can be applied into an application.

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