Clustering senior high school students’ numeracy score in Java using the K-means algorithm
DOI:
https://doi.org/10.24071/icre.v1i1.27Keywords:
clustering, Davies-Bouldin Index, K-Means algorithm, national assessment, numeracyAbstract
Numeracy skill is a key ability that supports critical thinking and is assessed in Indonesia through the Minimum Competency Assessment. This study aims to (1) analyze high school students' numeracy skills in Java based on four numeracy domains and three cognitive levels; and (2) cluster students’ numeracy skills using the K-Means algorithm with the Davies-Bouldin Index (DBI) as the evaluation metric. The data were obtained from the education data portal for the year 2021–2023. The process went through several stages: data cleaning, normalization, feature selection, clustering, and validation using the Davies–Bouldin Index (DBI). The results indicate a steady improvement in students’ numeracy performance, with average scores increasing from 51.18 in 2021, 54.08 in 2022, and 59.19 in 2023. The clustering process resulted in four clusters in 2021 and 2022 and two clusters in 2023. The clustering results revealed four distinct groups in 2021 and 2022, representing high, moderate, fluctuating, and low numeracy performance across regions. In 2023, these merged into two clusters, indicating reduced disparity and greater uniformity in students’ numeracy achievement across Java. Furthermore, it provides insights for educators and policymakers to design targeted interventions to improve students' numeracy skills and reduce learning gaps across regions.
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Copyright (c) 2025 Daviana Widya Maurora Putri, Adhi Surya Nugraha (Author)
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