Proceedings of the 5th Vocational Education International Conference (VEIC-5 2023)

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Naive Bayes-based Drop Out Recommendation System in Vocational College

Authors
Kartika Candra Kirana1, *, Slamet Wibawanto1, Heru Wahyu Herwanto1, Wahyu Sakti Gunawan Irianto1, Wahyu Nur Hidayat1, Muhamad Aqshal1
1Department of Electrical Engineering and Informatics, Faculty of Engineering, Universitas Negeri Malang, Malang, Indonesia
*Corresponding author. Email: kartika.candra.ft@um.ac.id
Corresponding Author
Kartika Candra Kirana
Available Online 6 February 2024.
DOI
10.2991/978-2-38476-198-2_99How to use a DOI?
Keywords
Drop out; Naïve bayes; Recommendation; Artificial intelligences
Abstract

When a student does not pass the requirements to graduate from a vocational college, the drop out (DO) system is usually utilized. The Naive Bayes technique make it simple to learn how graduation requirements might be modelled on prior evidence. As a result, this study suggests utilizing Naive Bayes to develop a Drop Out Recommendation System. We used 210 test data and 840 training data from the Kaggle dataset “Prediction of dropping out of school” for the testing phase. The proposed approach uses the Bayes technique to predict a student`s likelihood of dropping out based on their GPA and course enrolment in two semesters. The two value categories, high and low, of the GPA range from 1.6 to 4.6. However, the standard for the courses that are enrolled is based on the middle of the credit range, which runs from 5 to 20. The effectiveness of the Bayes Method is assessed using accuracy calculations. The test data shows five out of the seven data models. The test yielded 197 correct test results out of 210, with a maximum accuracy of 93.8%. It can be concluded that the Bayes technique can be used to recommend dropout strategies.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 5th Vocational Education International Conference (VEIC-5 2023)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
6 February 2024
ISBN
10.2991/978-2-38476-198-2_99
ISSN
2352-5398
DOI
10.2991/978-2-38476-198-2_99How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Kartika Candra Kirana
AU  - Slamet Wibawanto
AU  - Heru Wahyu Herwanto
AU  - Wahyu Sakti Gunawan Irianto
AU  - Wahyu Nur Hidayat
AU  - Muhamad Aqshal
PY  - 2024
DA  - 2024/02/06
TI  - Naive Bayes-based Drop Out Recommendation System in Vocational College
BT  - Proceedings of the 5th Vocational Education International Conference (VEIC-5 2023)
PB  - Atlantis Press
SP  - 727
EP  - 733
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-198-2_99
DO  - 10.2991/978-2-38476-198-2_99
ID  - Kirana2024
ER  -