K-Means Clustering of Student Mid-Term and Final Exam Data
Abstract
This study examines the use of the k-means clustering method in grouping students based on UAS and UTS scores to identify patterns of academic achievement. Clustering is an effective data mining technique for grouping data based on similar characteristics. By applying the k-means algorithm, this study aims to make it easier for lecturers to identify student abilities, so that they can provide appropriate support to those who need help. Data were taken from UTS and UAS scores of students at a university in Indonesia, and the results of the analysis showed that k-means clustering can group students according to their level of achievement. These findings are expected to help in the development of more effective teaching strategies and interventions, improving the quality of education and overall academic performance of students.
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