Perbandingan Algoritma K-means, Fuzzy C-means, dan Hierarchical clustering pada Klasterisasi Tingkat Pemahaman Siswa dalam Pembelajaran Berdiferensiasi - Ripai, Ahmad; Baskoro, Yudha; Imelda (Jurnal Algoritma, 2025)
Differentiated learning is a teaching and learning process tailored to the unique characteristics of each student. The main challenge in implementing differentiated learning lies in teachers’ difficulty in understanding the varying levels of students’ comprehension, particularly in informatics subjects. This study aims to compare the K-means, Fuzzy C-means, and Hierarchical Clustering algorithms in grouping students based on academic performance, using daily scores, knowledge, and skill scores of grade X students at SMK Citra Nusantara. The contribution of this research is the application of weighting on the parameters used to improve clustering results. The assigned weights are 0.2 for daily scores, 0.4 for knowledge, and 0.4 for skills when using three parameters, giving a smaller weight to daily scores. If only two parameters are used, the weighting is adjusted so that the total weight equals 1. The evaluation metrics applied are the Silhouette Index, Calinski-Harabasz Index, and Davies-Bouldin Index. The results show that the K-means method outperforms Fuzzy C-means and Hierarchical Clustering. The Silhouette Index is considered the most ideal because it evaluates two aspects simultaneously: cohesion (compactness within clusters) and separation (distinctness between clusters). However, a combination of the Silhouette Index, Davies-Bouldin Index, and Calinski-Harabasz Index is still required to achieve optimal results. The testing results for K-means, evaluated using the Silhouette Index, Davies-Bouldin Index, and Calinski-Harabasz Index, respectively, are 0.350125, 0.822452, and 1157.806. The best clustering outcome with two clusters categorized students into “very good” and “good” groups.