Pemetaan Kemampuan Matematis Peserta International Kangaroo Mathematics Contest Menggunakan K-Means Clustering
Abstract
Heterogenitas kemampuan matematis siswa Indonesia memerlukan pendekatan berbasis data untuk merancang pembelajaran yang lebih tepat sasaran. Penelitian ini bertujuan memetakan kemampuan matematis peserta Indonesia dalam International Kangaroo Mathematics Contest (IKMC) 2024 menggunakan K-Means Clustering pada enam tingkatan: Pre Ecolier (kelas 1-2), Ecolier (kelas 3-4), Benjamin (kelas 5-6), Cadet (kelas 7-8), Junior (kelas 9-10), dan Student (kelas 11-12). Data 92.529 peserta dianalisis melalui pembersihan data, standardisasi z-score, penentuan cluster optimal dengan Elbow Method dan Silhouette Score, kemudian pemodelan K-Means secara global dan per tingkatan. Hasil menunjukkan tiga cluster optimal pada setiap tingkatan dengan Silhouette Score 0.58-0.64, mengindikasikan kualitas clustering yang baik. Secara global, 48.5% peserta berada pada kategori rendah, 41.6% sedang, dan 9.9% tinggi. Analisis per tingkatan mengungkap dominasi cluster rendah-sedang (80-90%) dengan gap skor melebar dari 33,5 poin (Pre Ecolier) menjadi 44,2 poin (Cadet), menunjukkan divergensi kemampuan yang semakin tajam pada jenjang lebih tinggi. Clustering berbasis data kompetisi terbukti efektif memetakan heterogenitas kemampuan matematis secara representatif dan memberikan dasar empiris untuk pengembangan pembelajaran terdiferensiasi serta pembinaan matematika berbasis data.
References
Aggarwal, C. C., & Reddy, C. K. (2014). Data clustering: Algorithms and applications. Boca Raton, FL: CRC Press.
Akveld, M., Cáceres, L., Nieto, J. H., & Sánchez, R. (2020). The Math Kangaroo competition. Espacio Matemático, 1(2), 74-91.
Association Kangourou Sans Frontières. (2024). International Kangaroo Mathematics Contest: Regulations and philosophy.
Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. A. Larusson & B. White (Eds.), Learning analytics: From research to practice (pp. 61-75). Springer.
Dutt, A., Ismail, M. A., & Herawan, T. (2017). A systematic review on educational data mining. IEEE Access, 5, 15991-16005.
Freiman, V., & Leikin, R. (2021). Problem-solving strategies and mathematical giftedness: Insights from competition data. ZDM Mathematics Education, 53(7), 1535-1548.
Geary, D. C., Hoard, M. K., Nugent, L., & Bailey, D. H. (2019).
Mathematical cognition deficits in children with learning difficulties. Journal of Learning Disabilities, 52(2), 110–123.
Han, S., & Kim, J. (2023). Comparative analysis of clustering algorithms for student performance data in mathematical competitions. Education and Information Technologies, 28(3), 3421-3445.
Leikin, R. (2021). When practice needs more research: The nature and nurture of mathematical giftedness. ZDM Mathematics Education, 53(7), 1579-1589.
Liao, X., Jiang, S., & Liu, Y. (2021). Educational data mining: A bibliometric analysis of an emerging field. Interactive Learning Environments, 31(6), 3520-3536.
Nelson, G., & Powell, S. R. (2018). A systematic review of instructional interventions for students with mathematics difficulties. Journal of Learning Disabilities, 51(2), 131–147.
Nuraeni, R., Suryadi, D., & Turmudi. (2023). Clustering analysis of students’ mathematical problem-solving abilities. Journal of Mathematics Education, 14(2), 245–258.
Organisation for Economic Co-operation and Development. (2023). PISA 2022 results (Volume I): The state of learning and equity in education. OECD Publishing.
Romero, C., & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355.
Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53-65.
Saputra, R. R. H., & Rahmah, H. (2025). Performa siswa sekolah Islam dalam International Kangaroo Mathematics Contest. JPG: Jurnal Pendidikan Guru, 6(4), 770–778.
Schoenfeld, A. H. (2016). Learning to think mathematically: Problem solving, metacognition, and sense making in mathematics. Journal of Education, 196(2), 1-38.
Setiawan, W., & Kusumah, Y. S. (2021). Pemetaan kemampuan matematika siswa berdasarkan hasil ujian nasional menggunakan K-Means clustering. Jurnal Pendidikan Matematika, 15(1), 67–78.
Tungson, T. T. T., & Saputra, R. R. H. (2024). Efektivitas metode pembayaran “seikhlasnya” dan uji soal matematika nalaria realistik dalam meningkatkan partisipasi dan kemampuan matematika siswa sekolah-sekolah Islam di Surabaya. JPG: Jurnal Pendidikan Guru, 5(2), 278–284.
Xu, D., Wang, H., & Wang, M. (2019). A conceptual model for K-Means clustering of student performance data in mathematics education. Educational Technology & Society, 22(4), 122-135.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors grant the journal copyright of the work licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
