Improving the quality of the clustering process on students' performance using feature selection

Yuni Yamasari; Anita Qoiriah; Hapsari P. A. Tjahyaningtijas; Ricky E. Putra; Agus Prihanto; Asmunin

doi:10.1109/iSemantic50169.2020.9234249

Improving the quality of the clustering process on students' performance using feature selection

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Yuni Yamasari, Anita Qoiriah, Hapsari P. A. Tjahyaningtijas, Ricky E. Putra, Agus Prihanto, Asmunin

2020 Proceedings - 2020 International Seminar on Application for Technology of Information and Communication: IT Challenges for Sustainability, Scalability, and Security in the Age of Digital Disruption, iSemantic 2020 Conference paper Cited by 2

Abstract

the quality of students' performance clusters relates to the accuracy of students being in groups based on their performance. However, the resulting quality sometimes needs to be improved because the clustering process involves features that are not dominant. Furthermore, in the previous works, measurement of the quality of the clusters in unsupervised evaluation often only uses one measure. Therefore, this paper focuses to enhance the quality of clusters by eliminating features that are irrelevant by applying the feature selection method called the Gini Index. Meanwhile, in this paper, the clustering method applied is K-means for the mining process. Then, we propose the evaluation process measured by three metrics, namely: Silhouette coefficient, ANOVA, and t-test. The experimental results show that the Gini Index can improve the quality of clusters based on the three proposed metrics. © 2020 IEEE.

Affiliations

Department of Informatics, Universitas Negeri Surabaya, Surabaya, Indonesia; Department of Electrical Engineering, Universitas Negeri Surabaya, Surabaya, Indonesia

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