Implementation of the K-Medoids Algorithm for Grouping Students Based on Their Engagement in the Learning Process
DOI:
https://doi.org/10.71302/jbidai.v7i1.38Keywords:
activity, clustering, k-medoids algorithm, learning, studentsAbstract
Grouping students based on their level of engagement is an effective strategy to improve the quality of learning. SMP 9 Tarakan currently does not have a system that can group students based on their engagement in the learning process, which could assist in evaluating learning outcomes. In the initial stage of applying this method, the data collected came from the report card grades of 8th-grade students (Class VIII I) in the 2nd semester (Even Semester) of the 2022/2023 academic year. The characteristics used in the analysis include grades in Religion, Civic Education (PPKn), Mathematics, Science (IPA), Social Studies (IPS), Indonesian Language, English, Physical Education (Penjaskes), and Cultural Arts and Skills, with a total of 31 data points analyzed. The second step is to determine the number of clusters. The third step involves randomly selecting clusters with an initial medoid. The fourth step is to calculate the distance for each student using the Euclidean distance method, then mark the nearest distance and calculate the total distance. The fifth step is to calculate the total deviation (S) and use the Davies-Bouldin Index (DBI) to find the optimal value of k by conducting tests five times with k=3. Based on the calculation results, the analysis of student data grouping produced three clusters using Euclidean distance and Davies-Bouldin Index calculations. The results show that 3 students fall into the Highly Interested cluster, 4 students into the Interested cluster, and 24 students into the Less Interested cluster.
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