Comparison of Decision Tree, KNN and Naïve Bayes Methods In Predicting Student Late Graduation In the Informatics Engineering Department, Institute Business XYZ

Yunianto, Imam
Kurniawan, Ade
Mutoffar , Muhamad Malik

Adpebi International Journal of Multidisciplinary Sciences
Vol. 1 No. 1 (2022)

Solving the problem of student late graduation has been a lot of research done before, with various methods and algorithms. Likewise, the comparison of various methods to predict student graduation. However, there is no comparison of the Naïve Bayes, Decision Tree, and KNN methods using data from the Informatics Engineering Department in Institute Business XYZ. From this study by comparing the three methods, the Naïve Bayes method is ranked first with an accuracy rate of 66.67%, Precison 80% and Recall 66.67%. Rank 2 is the KNN algorithm with an accuracy rate of 55.56%, Precision 66.67% and Recall 66.67% and the last is the Decision Tree algorithm with an accuracy rate of 46%, Precison 48.3% and Recall 61.67%