Student performance classification using support vector machine (SVM) with polynomical kernel on online student activities / Muhammad Hareez Mohd Zaki ... [et al.]
The increasing usage of classification algorithms has encouraged researchers to explore many topics, including academic-related topics. In addition, the availability of data from various academic information management systems in recent years has been increasing, causing classification to become a t...
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UiTM Press,
2023-10.
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| LEADER | 00000 am a22000003u 4500 | ||
|---|---|---|---|
| 001 | repouitm_86032 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Mohd Zaki, Muhammad Hareez |e author |
| 700 | 1 | 0 | |a Abdul Aziz, Mohd Azri |e author |
| 700 | 1 | 0 | |a Sulaiman, Suhana |e author |
| 700 | 1 | 0 | |a Hambali, Najidah |e author |
| 245 | 0 | 0 | |a Student performance classification using support vector machine (SVM) with polynomical kernel on online student activities / Muhammad Hareez Mohd Zaki ... [et al.] |
| 260 | |b UiTM Press, |c 2023-10. | ||
| 500 | |a https://ir.uitm.edu.my/id/eprint/86032/1/86032.pdf | ||
| 520 | |a The increasing usage of classification algorithms has encouraged researchers to explore many topics, including academic-related topics. In addition, the availability of data from various academic information management systems in recent years has been increasing, causing classification to become a technique that is in demand by educational institutes. Thereby, having a classification technique is important in researching the data on students' performance. The purpose of this study is to classify students' performance by using a polynomial kernel of Support Vector Machine (SVM) on online students' activities. A new dataset is proposed in this study, which consists of academic and student online behaviours that influence the students' performance. The proposed dataset also undergoes pre-processing stage to improve the accuracy and identify the significance of the proposed features. The experiment for SVM-POLY classification performance was set with a range of values on the parameters to be optimised by an optimisation algorithm, Grid Search. Classification accuracy, Precision, Recall and f1-score were applied to observe the result and determine the best classifier performance. The experimental results show that SVM - POLY, with a gamma value of 0.005, regularisation value of 0.1 and degree value of 1, come out with the best performance compared to a default value of SVM - POLY. The study is significant towards educational data mining in analysing the students' performance during online students' activities. | ||
| 546 | |a en | ||
| 690 | |a Higher Education | ||
| 690 | |a Algorithms | ||
| 655 | 7 | |a Article |2 local | |
| 655 | 7 | |a PeerReviewed |2 local | |
| 787 | 0 | |n https://ir.uitm.edu.my/id/eprint/86032/ | |
| 856 | 4 | 1 | |u https://ir.uitm.edu.my/id/eprint/86032/ |z Link Metadata |