Rianto, Rianto and Fachrie, Muhammad Development of Educational Data Mining Model for Predicting Student Punctuality and Graduation Predicate. International Journal of Technology and Engineering Studies.
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Abstract
This paper discusses the Educational Data Mining (EDM) to predict the punctuality and graduation predicate. Both are considered as important aspects that represent the student’s academic performance. The model was developed by using academic records of 100 students from the vocational school of Informatics Management at Universitas Teknologi Yogyakarta. The dataset consisting of three features and two different labels was obtained by creating an Application Programming Interfaces (APIs) that connected to an academic database. Two classification algorithms were used to obtain knowledge from the dataset, i.e., Support Vector Machine (SVM) and Naive Bayes (NB). From the observations, SVM achieved the level of accuracy for punctuality of graduation on 0.68 while NB on 0.60. On graduation predicate, both algorithms achieved the same accuracy level on 0.92. Keywords: EDM, graduation predicate, NB, punctuality, SVM
| Item Type: | Article |
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| Depositing User: | S.T., M.Cs Muhammad Fachrie |
| Date Deposited: | 23 Jul 2021 04:56 |
| Last Modified: | 05 Jan 2022 00:40 |
| URI: | http://eprints.uty.ac.id/id/eprint/7752 |
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