Implementation of the K-Means Algorithm for Clustering Students’ Web Programming Course Grades Using Silhouette Score
Abstract
The development of information technology requires students majoring in informatics engineering to master web programming as one of the core competencies of the study program. Variations in students' ability to understand the material are reflected in significant differences in grades, so an objective analysis approach is needed to determine the ability of students. This study aims to group students based on academic grades in Web Programming courses using the K-Means algorithm. The data analyzed includes 1-3 assignment grades, attendance, UTS, and UAS from 32 students in the Department of Informatics Engineering, University of Papua. The research stages include preprocessing, data normalization, and clustering process using Orange Data Mining tools. Determination of the optimal number of clusters is done using the Silhouette Score method, and the best results are obtained at K = 4 with a Silhouette Score value of 0.513 which indicates a good cluster structure. The clustering results show that Cluster 1 has the highest score with a final score ranging from 0.93-1 with an Excellent score category consisting of 8 students, Cluster 2 with a Poor score category consists of 10 students with a final score range of 0.23-0.61, then Cluster 3 with a Good score category consists of 10 students with a Final score of 0.78-0.87 and Cluster 4 with a Fair score category consists of 4 students with a score range of 0.64-0.75. The results of this study provide information about the distribution of student abilities and can be the basis for improving learning strategies in the future.
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