A Review of Students’ Graduation Classification: A Comparison of Naive Bayes Classifier and K-Nearest Neighbour

DOI

10.2991/assehr.k.200303.052How to use a DOI?

Keywords

NBC, KNN, student graduation, classification

Abstract

Students are the most crucial aspects in determining the successful implementation of every program offered within educational institutions. Monitor the progress and students’ achievement, enhance the ability of students, consider the number of students who have graduated, the ratio of the total number of students, the competence of graduates, are several major factors that require attention and serious consideration from the higher educational institutions. This study is mainly based on the data mining technique by implementing two common algorithms namely Naive Bayes Classifier and K-Nearest Neighbour due to classifying students’ graduation (on time and overtime). The main objectives of this paper are to compare the achievement of both algorithms (NBC and KNN) towards students’ graduation classification. This paper also focuses to identify the most important variables to predict students’ performance. Leading to two categories of dependent variables namely graduate on time or graduate overtime. The considerations of these variables are based on the importance of each towards classifying the target. A Cross-validation technique is applied to evaluate both algorithms. This study is beneficial for the center of graduate studies, educators, policymakers, and others in order to identify the main factors that impact the students’ graduation status in higher educational institutions.

Copyright

© 2020, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Via Tuhamah Fauziastuti
AU  - Lilis Aslihah Rakhman
PY  - 2020
DA  - 2020/03/06
TI  - A Review of Students’ Graduation Classification: A Comparison of Naive Bayes Classifier and K-Nearest Neighbour
BT  - Proceedings of the 1st International Multidisciplinary Conference on Education, Technology, and Engineering (IMCETE 2019)
PB  - Atlantis Press
SP  - 219
EP  - 221
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.200303.052
DO  - 10.2991/assehr.k.200303.052
ID  - Fauziastuti2020
ER  -