Analisis Komparatif K-Means dan DBSCAN pada Data Nilai Siswa SMP Bina Bangsa Surabaya
DOI:
https://doi.org/10.33998/processor.2026.21.1.2636Keywords:
K-Means, DBSCAN, Clustering, Nilai Siswa, Remedial, Analisis DataAbstract
This study aims to analyze and compare the effectiveness of the K-Means and DBSCAN algorithms in clustering student score data for Grade VIII B students at SMP Bina Bangsa Surabaya. The dataset consists of 24 students and includes three main subjects: Mathematics, Indonesian Language, and Natural Sciences (IPA). Using a quantitative approach with a comparative experimental method, data preprocessing, normalization, and algorithm implementation were conducted on the Google Colab platform. The results show that the K-Means algorithm performs optimally with three clusters representing low, medium, and high academic performance, supported by the highest Silhouette Score at k = 3. Meanwhile, DBSCAN generated two main clusters, namely Remedial (17 students) and Non-Remedial (6 students), and identified two noise data points representing students with significantly different score patterns. These findings indicate that clustering methods are effective in helping teachers identify students’ learning needs and design more targeted academic interventions. Overall, the application of clustering proves beneficial in managing academically heterogeneous classrooms and enhancing the efficiency of instructional strategies..
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DOI:
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