Prediction Graduate Student Use Naive Bayes Classifier

DOI

10.2991/aisr.k.200424.056How to use a DOI?

Keywords

predicted, data mining, Naïve Bayes classifier

Abstract

Student graduation is important in the accreditation assessment process. Because student graduation there are standards that must be Achieved by the Study Program items, namely a four-year study period and a 3.0 GPA. Therefore we need a prediction that can Anticipate from the beginning of the graduation standard level that has been set. This study aims to predict student graduation using Naïve Bayes Classifier with a data mining approach. Naïve Bayes provides accurate prediction results with a minimum error rated compared to all other data mining components. With the prediction of the student, graduation can be used as input, especially the Information System Study Program in making policies for improvement in the future. The software used in data processing is WEKA. The test results showed that from the Information Systems Study Program Faculty of Computer Science Faculty of Sriwijaya University in 2015 as many as 141 students as training data and in 2016 as many as 127 students as testing data, the prediction accuracy was 97,6378%.

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  - Allsela Meiriza
AU  - Endang Lestari
AU  - Pacu Putra
AU  - Ayu Monaputri
AU  - Dini Ayu Lestari
PY  - 2020
DA  - 2020/05/06
TI  - Prediction Graduate Student Use Naive Bayes Classifier
BT  - Proceedings of the Sriwijaya International Conference on Information Technology and Its Applications (SICONIAN 2019)
PB  - Atlantis Press
SP  - 370
EP  - 375
SN  - 1951-6851
UR  - https://doi.org/10.2991/aisr.k.200424.056
DO  - 10.2991/aisr.k.200424.056
ID  - Meiriza2020
ER  -