Student Clustering Based on Academic Performance Using the K-Means Algorithm in RapidMiner: A Case Study with the Student Academic Performance Dataset
Keywords:
K-Means, clustering, RapidMiner, data miningAbstract
One of the primary indicators used to convey the effectiveness of the learning process and to create more efficient teaching methods is student academic performance. This study uses the RapidMiner application to use the K-Means Clustering method in order to group students according to their academic performance. The synthetic data, which includes details about student involvement, attendance rates, and academic grades, is taken from the Kaggle platform. This study was carried out in a number of steps, including cluster quality assessment, attribute selection, algorithm application, and data pre-processing.Based on the results, three student groups with characteristics of high, medium, and low academic performance were examined. The Davies-Bouldin Index examination indicated that the clustering results were optimal. These findings are expected to serve as a guide for educational institutions to develop more appropriate and successful teaching strategies.
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Copyright (c) 2025 Siti Khodijah, Athaya Rima Hariyanto, Berliani Salsabiilah, Winona Septi Aulia, Maulana Fansyuri (Author)
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