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

Siti Maghfirotul Ulyah, Marisa Rifada, Elly Ana
2020 Proceedings of the 2nd International Seminar on Science and Technology (ISSTEC 2019)   unpublished

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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
more » ... ironmental 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).
doi:10.2991/assehr.k.201010.022 fatcat:rlchhfwo7zczrcert2wqxeipvm
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Siti Maghfirotul Ulyah, Marisa Rifada, Elly Ana. "Classifying Student's Duration of Study in Faculty of Science and Technology UNAIR Using Naïve Bayes and Neural Network Classifiers." Proceedings of the 2nd International Seminar on Science and Technology (ISSTEC 2019) (2020)