Application of K-Means Clustering Method Using RapidMiner for Student Achievement Classification in Private Schools
Keywords:
K-Means Clustering, RapidMiner, academic achievement, student clustering, educational strategyAbstract
This study discusses the application of the K-Means Clustering algorithm in grouping the level of academic achievement of students in private schools with the help of RapidMiner software. The data analyzed include assignment scores, midterm exams, final exams, and attendance. The K-Means algorithm was chosen because of its ability to group unlabeled numeric data and recognize hidden patterns in the dataset. The analysis was carried out on data from 5,000 students obtained through the Kaggle platform. The clustering results produced two main groups, namely students with high academic achievement and students with lower achievements. This process allows schools to understand the characteristics of each group of students and develop more effective coaching strategies and educational policies. The use of RapidMiner has been proven to help the data analysis process efficiently and intuitively, without the need for advanced programming skills.
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Copyright (c) 2025 Eko Andri Wibowo, Ririn Aryanti (Author)
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