MODIFICATION OF K-MEANS AND K-MODE ALGORITHMS TO ENHANCE THE PERFORMANCE OF CLUSTERING STUDENT LEARNING STYLES IN THE LEARNING MANAGEMENT SYSTEM

Setyaningsih, Emy and Hidayat, Nurul and Lestari, Uning and Septiarini, Anindita (2023) MODIFICATION OF K-MEANS AND K-MODE ALGORITHMS TO ENHANCE THE PERFORMANCE OF CLUSTERING STUDENT LEARNING STYLES IN THE LEARNING MANAGEMENT SYSTEM. ICIC Express Letters, 17 (1). pp. 49-59. ISSN 1881-803X

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Abstract

During the Corona Virus Disease (COVID-19) pandemic, many access to learning used the e-learning system through the Learning Management System (LMS) platform. One of the weaknesses of the learning process through e-learning is that it cannot detect student learning styles based on actual behavior patterns during online learning. Most of the methods used to study automatic detection techniques use classification methods. One of the weaknesses of the classification method is the determination of class labels, so a learning style detection model was developed using the concept of clustering before classification to produce class labels with a high level of validation. This study focuses on increasing the validity of the clustering method by comparing the performance of the modified K-Means and K-Mode algorithms. The proposed modification of the two algorithms is carried out at the initial centroid determination stage. The performance of the two modified algorithms was carried out by measuring the validation values of the Davies-Bouldin Index (DBI) and Silhouette Index (SI) using log file data from 88 students taking computer programming courses. The validation results of the DBI and SI values indicate that the proposed model has better performance when implemented in the K-Mode algorithm than the K-Means algorithm.

Item Type: Article
Subjects: Z Bibliography. Library Science. Information Resources > ZA Information resources
Divisions: Fakultas Teknologi Informasi & Bisnis > Rekayasa Sistem Komputer (S1)
Depositing User: Emy Setyaningsih
Date Deposited: 17 Mar 2023 02:38
Last Modified: 23 Jun 2023 07:32
URI: http://eprints.akprind.ac.id/id/eprint/1736

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