Utilizing K-Means Clustering for Grouping Student Achievement Data to Evaluate Learning Activeness - Agusniar, Cut; Mulya Ulfa, Septia; Rhomadhona, Herfia (Journal of Advanced Computer Knowledge and Algorithms, 2025)
Learning basically aims to foster student activity and creativity through various learning experiences and interactions. Teachers are an important part of the process of improving the quality of education. In addition, the success of the learning process depends on student activity. The world of education needs to improve the quality of students and their performance by using existing facilities, infrastructure and human resources. One way information systems can be used to improve student achievement and quality is by analyzing grades based on students' academic abilities, discipline and way of behaving.The aim of this research is to group students based on academic scores, disciplinary scores and attitude scores using the K-Means Clustering algorithm, so that the cluster results can be used as a reference in improving student scores in the next learning process. In this research, the elbow method was used to determine the optimal number of clusters. Students will be grouped into clusters. Visualization and correlation analysis between value variables is carried out to provide further insight into the distribution of data and the relationship between its values.