Open AccessPrediksi Kelulusan dan Putus Studi Mahasiswa dengan Pendekatan Bertingkat pada Perguruan Tinggi
Author(s) -
Hermanto Hermanto
Publication year - 2021
Publication title -
jurnal sistem informasi dan manajemen basis data
Language(s) - English
Resource type - Journals
eISSN - 2621-0827
pISSN - 2615-7292
DOI - 10.30873/simada.v3i2.2359
Subject(s) - graduation (instrument) , decision tree , dropout (neural networks) , naive bayes classifier , computer science , k nearest neighbors algorithm , artificial intelligence , machine learning , mathematics , support vector machine , geometry
Currently, the problem of college failure, its on-time graduation, and the factors that cause it is still an interesting research topic (C. Marquez-Vera, C. Romero and S. Ventura, 2011). This study compares three data mining classification algorithms namely Naive Bayes, Decision Tree and K-Nearest Neighbor to predict graduation and dropout risk for students to improve the quality of higher education and the most accurate algorithms to use Prepare graduation and dropout prediction Student studies. The best algorithm for predicting graduation and dropout is the decision tree with the best accuracy value of 99.15% with a training data ratio of 30%. Keyword : Data Mining; Algoritma Naive Bayes; Decision Tree; K-Nearest Neighbor; Predict Graduation; Drop Out.