THE DEVELOPMENT OF A STUDENT CLUB RECOMMENDATION MODEL USING K-MEANS CLUSTERING AND DECISION TREE ALGORITHMS Research Article
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Abstract
This research aimed to (1) study the student grouping process using the K-Means Clustering technique, (2) develop a Decision Tree model for club recommendation, and (3) evaluate the effectiveness of the clustering process and the club recommendation model. The sample consisted of 171 undergraduate students from Southeast Bangkok University, selected using a simple random sampling method. The instrument was a questionnaire that was content-validated by three experts. The statistical analysis employed Label Encoding, K-Means clustering analysis, and determination of the optimal number of clusters using the Elbow Method, Davies–Bouldin Index, and Calinski–Harabasz Index.The results revealed that (1) the optimal number of clusters was four, (2) each group of students exhibited distinct interests and skills, and (3) the Decision Tree model achieved an average accuracy of 0.89, indicating high predictive accuracy. Machine learning (ML) techniques significantly improved the accuracy of grouping students and recommending clubs suitable for their interests. These findings suggest that machine learning techniques can effectively enhance personalized club recommendations and foster student engagement.
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This article is published and copyrighted by the Science and Technology Journal. South East Bangkok College
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