Open AccessIMPLEMENTASI KLASIFIKASI NAIVE BAYES DALAM MEMPREDIKSI LAMA STUDI MAHASISWA (STUDI KASUS : UNIVERSITAS DHYANA PURA)
Author(s) -
Kelvin Hennry Loudry Malelak,
I Made Dwi Ardiada,
Gerson Feoh
Publication year - 2021
Publication title -
sintech (science and information technology) journal
Language(s) - English
Resource type - Journals
eISSN - 2598-9642
pISSN - 2598-7305
DOI - 10.31598/sintechjournal.v4i2.964
Subject(s) - graduation (instrument) , naive bayes classifier , mathematics education , bayes' theorem , psychology , computer science , mathematics , statistics , artificial intelligence , bayesian probability , geometry , support vector machine
Under normal conditions, undergraduate or undergraduate students from a university can complete their studies for 4 years or 8 semesters. In fact, many students complete their study period of more than 4 years. Is known that in fact in the 2015/2016 academic year there were 744 people who were accepted as students. Of the 744 people who were accepted, 405 people had completed a study period of about 4 years and the remaining 39 people completed their studies for 5 years and 300 of them did not continue their studies. Based on the problem on, so This study implements a classification that can help Dhyana Pura University in predicting the length of study for students who are currently studying in various study programs at Dhyana Pura University. The author's method serves in the classification to predict long student study period is the Naive Bayes algorithm. By using the Java-based Rapid Miner tool to classify graduation data. Then the implementation of data mining which is divided into 968 training data and 193 data testing data with naive Bayes has succeeded in obtaining an accuracy rate of 100% which also has very good parameters.