Performance Comparison of Naïve Bayes and Neural Network in Predicting Student Violation

Harits Ar Rosyid; Utomo Pujianto; Bima Garis Invarian

doi:10.1109/EIConCIT50028.2021.9431920

Performance Comparison of Naïve Bayes and Neural Network in Predicting Student Violation

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Harits Ar Rosyid, Utomo Pujianto, Bima Garis Invarian

2021 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021 Conference paper Cited by 2

Abstract

Education is a process of building human character to become more civilized and knowledgeable the purpose of education is to enable students to develop and control themselves. However, in the process of implementing it, many students committed some violations therefore, this study aims to enable school authorities to easily predict students who tend to break the rules. In a previous study, the Naïve Bayes algorithm was widely used to make this kind of prediction therefore, this study will compare Naive Bayes with the Neural Network algorithm to determine which algorithm is the most optimal for predicting student tendency in committing a violation the experiment's accuracy of the Neural Network algorithm is slightly higher than the Naïve Bayes algorithm, which is 98.76% versus 97.84%. © 2021 IEEE.

Affiliations

Universitas Negeri Malang, Electrical Engineering Department, Malang, Indonesia

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