Modeling Student’s Academic Performance During Covid-19 Based on Classification in Support Vector Machine
| Autor: | N. A. F. Sulaiman, M. F. Zulfikri, Shazlyn Milleana Shaharudin, N. H. Zainuddin, M. F. Mohd Fuad, N. A. M. Samsudin |
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| Rok vydání: | 2021 |
| Předmět: |
Coronavirus disease 2019 (COVID-19)
Computer science business.industry General Mathematics Online learning Sigmoid function Machine learning computer.software_genre Education Support vector machine Computational Mathematics Computational Theory and Mathematics Polynomial kernel Kernel (statistics) Radial basis function kernel Artificial intelligence business computer Movement control |
| Zdroj: | Turkish Journal of Computer and Mathematics Education (TURCOMAT). 12:1798-1804 |
| ISSN: | 1309-4653 |
| DOI: | 10.17762/turcomat.v12i5.2190 |
| Popis: | This study proposed a statistical investigate the pattern of students’ academic performance before and after online learning due to the Movement Control Order (MCO) during pandemic outbreak and a modelling students’ academic performance based on classification in Support Vector Machine (SVM). Data sample were taken from undergraduate students of Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris (UPSI). Student’s Grade Point Average (GPA) were obtained to developed model of academic performances during Covid-19 outbreak. The prediction model was used to predict the academic performances of university students when online classes was conducted. The algorithm of Support Vector Machine (SVM) was used to develop a model of students’ academic performance in university. For the Support Vector Machine (SVM) algorithm, there are two important parameters which are C (misclassification tolerance parameter) and epsilon need to identify before proceed the further analysis. The parameters was applied to four different types of kernel which is linear kernel, radial basis function kernel, polynomial kernel and sigmoid kernel and the result was found that the best accuracy achieved by SVM are 73.68% by using linear kernel and the worst accuracy obtained from a sigmoid kernel which is 67.99% with parameter of misclassification tolerance C is 128 and epsilon is 0.6. © 2021 Karadeniz Technical University. All rights reserved. |
| Databáze: | OpenAIRE |
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