Naive Bayes and Decision Tree Algorithms for BRI Life Sharia Insurance Product Classification

Abstract

Law 12 of 2012 mandates that the government increase access to higher education for high achievers and underprivileged people. One of the efforts to realize this is by providing KIP Lectures. To ensure that beneficiaries are eligible for KIP scholarships, it is necessary to classify scholarship recipients correctly using data mining classification techniques. The classification technique chosen is k-Nearest Neighbor (K-NN). K-NN is a classification method that relies heavily on the k parameter in carrying out classification. K-NN was applied to the KIP Scholarship applicant dataset at UIN Malang in 2022. The test scenario in this research is to compare the k-odd and k-even parameters to find the most optimal k value in K-NN. The highest accuracy value obtained by k-odd is 0.71 or 71% when k=9, and the highest for k-even is 0.67 or 67% when k=10. Using optimal k parameters is proven to improve k-NN performance. The K-NN algorithm with k-odd parameters, namely k=9, is the best method for classifying KIP scholarship recipients in this research. The results of this research can be considered in determining KIP scholarship recipients worthy of using K-NN.