Benchmarking Naïve Bayes and ID3 Algorithm for Prediction Student Scholarship

Student scholarship through Indonesian smart cards (ISC) is a cash donation scholarship for all student in range 6-21 years old as a solution of destitute child or potential dropouts. Currently the large student data stored in educational database. The data was used to determine feasible receiver ISC. Manual prediction by human take long time and potentially human error. This paper aim to predict feasible receiver ISC by using educational data mining. Source of data come from a senior high school database in Riau Province, Indonesia. In this paper we compared two algorithms (Naïve Bayes and ID3) to predict receiver ISC. Eighty percent process in this paper is data pre-processing. Information of other scholarship is the most influences variable to predict student scholarship. ID3 algorithm classification has accuracy 83 percent and f1 score 71 percent. While Naive Bayes Classification has accuracy 89 percent and f1 score 72 percent. In this case Naïve Bayes algorithm is better in ID3 algorithm.