K-Means Clustering with Elbow Method and Validity Indices for Classifying Student Academic Achievement Based on Knowledge Scores at SDN 48 Kota Jambi
DOI:
https://doi.org/10.52436/1.jutif.2026.7.1.5349Keywords:
Academic Achievement, Clustering, Data Mining, Elementary Education, K-MeansAbstract
Student performance evaluation at SDN 48 Kota Jambi has been traditionally conducted manually, which is inefficient and often subjective. This study aims to provide an objective classification of students’ academic achievement using data-driven methods. The research applies the Knowledge Discovery in Databases (KDD) framework, which involves data selection, preprocessing, clustering, and evaluation. The dataset consists of knowledge scores from 152 elementary students across seven subjects, obtained from the Merdeka Curriculum report cards. Data preprocessing included cleaning and normalization to ensure consistency. K-Means clustering was implemented using RapidMiner, with the optimal number of clusters determined through the Elbow Method. Cluster validity was assessed using the Davies–Bouldin Index (1.226) and the Silhouette Coefficient (0.245). The results produced three clusters: high achievers (30.9%), medium achievers (27.0%), and low achievers (42.1%). Centroid analysis indicated that Mathematics and Physical Education were the most discriminative subjects across groups. These findings highlight a substantial proportion of students requiring remedial intervention and support differentiated learning strategies. The contribution of this research lies in applying educational data mining techniques to an elementary school context in Jambi, integrating both quantitative indices and qualitative validation with teachers. The study demonstrates that clustering methods can enhance educational decision-making, providing a basis for adaptive teaching, targeted interventions, and resource allocation in elementary education.
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