Open AccessEducational Data Mining for Predicting Student Graduation Using the Naïve Bayes Classifier Algorithm
Author(s) -
Edi Sutoyo,
Ahmad Almaarif
Publication year - 2020
Publication title -
jurnal resti (rekayasa sistem dan teknologi informasi)
Language(s) - English
Resource type - Journals
ISSN - 2580-0760
DOI - 10.29207/resti.v4i1.1502
Subject(s) - graduation (instrument) , naive bayes classifier , drop out , computer science , mathematics education , classifier (uml) , higher education , recall , machine learning , medical education , psychology , artificial intelligence , mathematics , political science , medicine , support vector machine , economics , law , demographic economics , cognitive psychology , geometry
The quality of students can be seen from the academic achievements, which are evidence of the efforts made by students. Student academic achievement is evaluated at the end of each semester to determine the learning outcomes that have been achieved. If a student cannot meet certain academic criteria that are stated by fulfilling the requirements to continue his studies, the student may have the potential to not graduate on time or even Drop Out (DO). The high number of students who do not graduate on time or DO in higher education institutions can be minimized by detecting students who are at risk in the early stages of education and is supported by making policies that can direct students to complete their education. Also, if the time for completion of student studies can be predicted then the handling of students will be more effective. One technique for making predictions that can be used is data mining techniques. Therefore, in this study, the Naive Bayes Classifier (NBC) algorithm will be used to predict student graduation at Telkom University. The dataset was obtained from the Information Systems Directorate (SISFO), Telkom University which contained 4000 instance data. The results of this study prove that NBC was successfully implemented to predict student graduation. Prediction of the graduation of these students is able to produce an accuracy of 73,725%, precision 0.742, recall 0.736 and F-measure of 0.735.