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Implementation of Fuzzy C-Means to Determine Student Satisfaction Levels in Online Learning
Abstract
Online learning is the best solution during the COVID-19 pandemic, causing a difference in the learning process that lecturers usually do face-to-face with students. Changes in the learning process are expected to be implemented effectively and efficiently. The purpose of this study is to determine how the level of student satisfaction in online learning and produce a system that can cluster the level of student satisfaction using the Fuzzy C-Means method. The variables used in this study have 3 parameters, namely the teaching and learning process, lecturer competence, facilities, and infrastructure. The stages carried out in this study were collecting questionnaire data filled out by 500 students of Informatics Engineering, Malikussaleh University, the data obtained was processed based on the average student answers. Based on the data that has been obtained will form 2 clusters, namely satisfied and dissatisfied in online learning using the Fuzzy C-Means method. The results of clustering of 500 data obtained that 303 students felt "satisfied" and as many as 197 students felt "not satisfied" in the online learning carried out, for cluster 1 (satisfied students) with an average value of the teaching and learning process 7,389, lecturer competence 7,863 and facilities and infrastructure 7,8883. Cluster 2 (students are not satisfied) with the average value of teaching and learning process 4,466, lecturer competence 5,069, and the value of facilities and infrastructure 5,424. The contribution of this research is to help the manager of the Department of Information Engineering, Malikussaleh University to evaluate and improve the online learning process.
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DOI: https://doi.org/10.32520/stmsi.v11i1.1638
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