Comparison of Naive Bayes Method, K-NN (K-Nearest Neighbor) and Decision Tree for Predicting the Graduation of ‘Aisyiyah University Students of Yogyakarta


Mail Tikaridha Hardiani(1*)

(1) Department of Information Technology, Science and Technology Faculty, University ‘Aisyiyah Yogyakarta, Indonesia
(*) Corresponding Author
10.31101/ijhst.v2i1.1829| Abstract views : 571 | PDF views : 141


The students of Universitas ‘Aisyiyah Yogyakarta have been increasing including the number of students in the Faculty of Health Sciences. In 2016 the total number of UNISA students was 1851. The increasing number of students every year leads to great numbers of data stored in the university database. The data provide useful information for the university to predict student graduation or student study period whether they graduate on time with a study period of 4 years or late with a study period of more than 4 years. This can be processed by using a data mining technique that is the classification technique. Data needed in the classification technique are data of students who have graduated as training data and data of students who are still studying in the university as testing data. The training data were 501 records with 10 goals and the testing data were 428 records. Data mining process method used was the Cross-Industry Standard Prosses for Data Mining (CRISPDM). The algorithms used in this study were Naive Bayes, K-Nearest Neighbor (KNN) and Decision Tree. The three algorithms were compared to see the accuracy by using Rapidminer software. Based on the accuracy, it was found that the K-NN algorithm was the best in predicting student graduation with an accuracy of 91.82%. The K-NN algorithm showed that 100% of the students of Nursing study program of Universitas Aisyiyah Yogyakarta are predicted to graduate on time.


data mining, prediction, student, graduation, decicion tree, naive bayes, K-NN

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DOI: https://doi.org/10.31101/ijhst.v2i1.1829

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