Clustering Students' Level of Understanding of Information and Communication Technology Subjects using the K-Means Algorithm

Abstract

Information and Communication Technology (ICT) within the Merdeka Curriculum requires students not only to operate technology but also to understand basic computational concepts and problem-solving, so its success depends heavily on students' level of understanding. The problem is that at SMA Negeri 3 Binjai, students' understanding is uneven, and the available academic score data has not been processed to map patterns of understanding, making it difficult for teachers to determine appropriate learning interventions. As a solution, this study applies the K-Means Clustering algorithm to 634 student score records covering practicum assignment scores, midterm (UTS) scores, and final-exam (UAS) scores, which are transformed into categorical codes based on the Learning Objective Achievement Criteria (KKTP), standardized, and then grouped through the CRISP-DM stages using the Python programming language. The number of clusters is tested over the range K = 2 to 10 using the Silhouette Score and the Davies-Bouldin Index. The results establish K = 3 as the outcome because it is the most pedagogically actionable, with a Silhouette Score of 0.553 and a Davies-Bouldin Index of 0.849, dividing students into High (11.1%), Medium (70.0%), and Low (19.0%) understanding groups. In conclusion, K-Means can map students' level of understanding of ICT objectively, providing a basis for teachers to design more targeted learning strategies.