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Decision Support System for Choosing an Elective Course Using Naive Bayes Classifier

Wicaksana, Arya and Iswari, Ni Made Satvika and Abiyoga, Abiyoga (2019) Decision Support System for Choosing an Elective Course Using Naive Bayes Classifier. In: SNPD 2019: Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing.

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Official URL: https://link.springer.com/chapter/10.1007/978-3-03...

Abstract

Department of Informatics in Universitas Multimedia Nusantara offers five elective courses to the students. Choosing an elective course that is most suitable for the student interest and academic skills is dilemmatic. Thus, a decision support system is proposed to assist the students in choosing an elective course based on not only their interest but also academic skills. The system uses Naive Bayes Classifier and Laplace smoothing for the classification process. The data used for this research is collected from 120 students. Learning from past students records, the system could predict the outcome of the student upon choosing an elective course. The evaluation of the system shows that the accuracy of the system is 0.30 and 0.33, recall is 0.318 and 0.378, precision is 0.215 and 0.407, and the F-score are 0.257 and 0.390. A test for two classes and three classes classification using 60 generated data shows improvement in the performance with the accuracy of 0.83 and 0.72 and the F-score of 0.843 and 0.728.

Item Type: Conference or Workshop Item (Paper)
Subjects: 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 004 Computer Science (Cloud, Data Processing, Interface, E-mail, Social Media, Information Architecture, Hardware, Mobile Computing)
Divisions: Fakultas Teknik Informatika > Program Studi Informatika
Depositing User: mr admin umn
Date Deposited: 14 Oct 2021 07:57
Last Modified: 14 Oct 2021 07:57
URI: http://kc.umn.ac.id/id/eprint/18769

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