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Comparative Study of Naïve Bayes Algorithm and K-NN Algorithm in Predicting Timely Graduation

Mohammad Zainuddin, Achmad Noercholis

Abstract



One indicator that shows an effective educational process is the student's timely graduation rate. In this study, timely graduation is predicted using Naïve Bayes and k-Nearest Neighbor. Both algorithms are optimized using PSO . The results showed that the PSO-based k-Nearest Neighbor (k-NN) algorithm had better results than the Naïve Bayes algorithm, with an Accuracy value of 76.69%. The accuracy value before and after the addition of the PSO feature is higher than the k-NN algorithm compared to the Naïve Bayes algorithm. There are 6 attributes that have the same weighting value (value = 1,000) in the k-NN Algorithm, namely Gender, achievement index in Semester 2, achievement index in Semester 3, achievement index in Semester 4, achievement index in Semester 6 and Marriage Status. To find out which attributes are more influential than the 6 attributes, it can be helped by checking the weighting value on the comparison algorithm (Naïve Bayes), finally 2 attributes are obtained that mostly influence on students' timely graduation. They are the Gender attribute and Achievement Index in semester 6. The smallest weighting lies in the Age Attribute (value = 0,000) for both the Naïve Bayes Algorithm and the k-NN Algorithm. It means that the Age attribute does not have a strong influence on students' timely graduation.

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