Analyzing Students' Interest in Mathematics Through the Implementation of the K-Means Clustering Algorithm

• Published in: J-INTECH Vol. 13; no. 1; pp. 71 - 77
• Main Authors: Wintana, Dede, Sulaiman, Hamdun, Rohman, Ramdhan Saepul, Gunawan, Gunawan, Ghani, Muhammad Abdul
• Format: Journal Article
• Language: English
• Published: LPPM STIKI Malang 24.06.2025
• Subjects: Educational Data Mining; K-Means Clustering; Mathematics Interest; RapidMiner; Student Performance
• ISSN: 2303-1425; 2580-720X
• DOI: 10.32664/j-intech.v13i01.1861

This Research is motivated by the importance of understanding students' interest in mathematics, especially in State Junior High School 193 East Jakarta, considering that mathematics is often considered a difficult and frightening subject for some students. Learning interest, which is defined as the tendency of students to pay attention with a feeling of pleasure, has a significant influence on the process and results of student learning. This study aims to identify the level of student interest in mathematics using the K-Means algorithm. This method is used to group students into several clusters based on their level of interest. The results showed that students were divided into three clusters, namely the first cluster with very high interest totaling 193 students with an average Final Semester Exam score of 91.920, the second cluster with low interest totaling 18 students with an average score of 52.333, and the third cluster with high interest totaling 66 students with an average score of 87.606.