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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Main Authors:	Mohd Zaki, Muhammad Hareez, Abdul Aziz, Mohd Azri, Sulaiman, Suhana, Hambali, Najidah
Format:	Article
Language:	English
Published:	UiTM Press 2023
Subjects:	Higher Education Algorithms
Online Access:	https://ir.uitm.edu.my/id/eprint/86032/1/86032.pdf https://ir.uitm.edu.my/id/eprint/86032/
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Institution:	Universiti Teknologi Mara
Language:	English

id	my.uitm.ir.86032
record_format	eprints
spelling	my.uitm.ir.860322023-10-29T11:39:15Z https://ir.uitm.edu.my/id/eprint/86032/ Student performance classification using support vector machine (SVM) with polynomical kernel on online student activities / Muhammad Hareez Mohd Zaki ... [et al.] jeesr Mohd Zaki, Muhammad Hareez Abdul Aziz, Mohd Azri Sulaiman, Suhana Hambali, Najidah Higher Education Algorithms 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. UiTM Press 2023-10 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/86032/1/86032.pdf Student performance classification using support vector machine (SVM) with polynomical kernel on online student activities / Muhammad Hareez Mohd Zaki ... [et al.]. (2023) Journal of Electrical and Electronic Systems Research (JEESR) <https://ir.uitm.edu.my/view/publication/Journal_of_Electrical_and_Electronic_Systems_Research_=28JEESR=29/>, 23 (1): 9. pp. 80-90. ISSN 1985-5389
institution	Universiti Teknologi Mara
building	Tun Abdul Razak Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Mara
content_source	UiTM Institutional Repository
url_provider	http://ir.uitm.edu.my/
language	English
topic	Higher Education Algorithms
spellingShingle	Higher Education Algorithms Mohd Zaki, Muhammad Hareez Abdul Aziz, Mohd Azri Sulaiman, Suhana Hambali, Najidah Student performance classification using support vector machine (SVM) with polynomical kernel on online student activities / Muhammad Hareez Mohd Zaki ... [et al.]
description	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.
format	Article
author	Mohd Zaki, Muhammad Hareez Abdul Aziz, Mohd Azri Sulaiman, Suhana Hambali, Najidah
author_facet	Mohd Zaki, Muhammad Hareez Abdul Aziz, Mohd Azri Sulaiman, Suhana Hambali, Najidah
author_sort	Mohd Zaki, Muhammad Hareez
title	Student performance classification using support vector machine (SVM) with polynomical kernel on online student activities / Muhammad Hareez Mohd Zaki ... [et al.]
title_short	Student performance classification using support vector machine (SVM) with polynomical kernel on online student activities / Muhammad Hareez Mohd Zaki ... [et al.]
title_full	Student performance classification using support vector machine (SVM) with polynomical kernel on online student activities / Muhammad Hareez Mohd Zaki ... [et al.]
title_fullStr	Student performance classification using support vector machine (SVM) with polynomical kernel on online student activities / Muhammad Hareez Mohd Zaki ... [et al.]
title_full_unstemmed	Student performance classification using support vector machine (SVM) with polynomical kernel on online student activities / Muhammad Hareez Mohd Zaki ... [et al.]
title_sort	student performance classification using support vector machine (svm) with polynomical kernel on online student activities / muhammad hareez mohd zaki ... [et al.]
publisher	UiTM Press
publishDate	2023
url	https://ir.uitm.edu.my/id/eprint/86032/1/86032.pdf https://ir.uitm.edu.my/id/eprint/86032/
_version_	1781709309523001344

Student performance classification using support vector machine (SVM) with polynomical kernel on online student activities / Muhammad Hareez Mohd Zaki ... [et al.]

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