Classifying Student’s Duration of Study in Faculty of Science and Technology UNAIR Using Naïve Bayes and Neural Network Classifiers

DOI

10.2991/assehr.k.201010.022How to use a DOI?

Keywords

graduate, duration of study, classification, neural network, Naïve Bayes

Abstract

Timely graduation is one of the essential criteria for a university in the accreditation program. The objective of this study is to predict the duration of study based on several factors related to students. The data in this study were the data of Faculty of Science and Technology (FST) graduates for 11 years (2008-2018) but limited to the undergraduate degree. The department in FST includes Mathematics, Statistics, Information System, Chemistry, Biology, Physics, Biomedical Engineering, and Environmental Sciences and Technology. The attributes in this work are department, address, gender, high school status, high school national exam score, admission program, department selection order, parents’ income, GPA and ELPT. The dependent variable, study duration, is divided into two categories, which are a timely graduate (less or equal to 4 years) and untimely graduate (more than 4 years). The classification methods in predicting the period of study are Naïve Bayes and Neural Network. In this study, various percentages of training data and testing data will be compared. The results reveal that Naïve Bayes outperforms Neural Network in classification accuracy, even in the smaller sample.and their difference is statistically significant.

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  - Siti Maghfirotul Ulyah
AU  - Marisa Rifada
AU  - Elly Ana
PY  - 2020
DA  - 2020/10/11
TI  - Classifying Student’s Duration of Study in Faculty of Science and Technology UNAIR Using Naïve Bayes and Neural Network Classifiers
BT  - Proceedings of the 2nd International Seminar on Science and Technology (ISSTEC 2019)
PB  - Atlantis Press
SP  - 148
EP  - 158
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.201010.022
DO  - 10.2991/assehr.k.201010.022
ID  - Ulyah2020
ER  -