PENGEMBANGAN MEDIA PEMBELAJARAN MATEMATIKA ADAPTIF BERBASIS MACHINE LEARNING UNTUK MENINGKATKAN HASIL BELAJAR SISWA
DOI:
https://doi.org/10.54314/jssr.v9i3.6801Keywords:
adaptive learning media, machine learning, learning outcomes, mathematics, Random ForestAbstract
Abstract: This study aims to develop an adaptive mathematics learning media based on machine learning and to examine its feasibility and effectiveness in improving student learning outcomes. The media was developed using the 4D research and development model (define, design, develop, disseminate). A Random Forest algorithm was employed to classify students' ability into three categories (low, medium, high) based on their exercise history, allowing the media to automatically adjust the difficulty level of the exercises. The trial subjects were eighth-grade junior high school students of Tamansiswa Sukadamai. Data were collected through a needs analysis questionnaire, expert validation sheets, a System Usability Scale (SUS) questionnaire, and pretest-posttest learning outcome tests. The results indicate that the media is feasible, practical, and effective in improving student learning outcomes, and that the developed model shows good classification accuracy.
Keywords: adaptive learning media, machine learning, learning outcomes, mathematics, Random Forest
Abstrak: Penelitian ini bertujuan untuk mengembangkan media pembelajaran matematika adaptif berbasis machine learning serta menguji kelayakan dan efektivitasnya dalam meningkatkan hasil belajar siswa. Media dikembangkan menggunakan model penelitian dan pengembangan 4D (define, design, develop, disseminate). Algoritma Random Forest digunakan untuk mengklasifikasikan kemampuan siswa ke dalam tiga kategori (rendah, sedang, tinggi) berdasarkan riwayat pengerjaan soal, sehingga media dapat menyesuaikan tingkat kesulitan latihan secara otomatis. Subjek uji coba penelitian adalah siswa kelas VIII SMP Tamansiswa Sukadamai. Data dikumpulkan melalui angket analisis kebutuhan, lembar validasi ahli, angket System Usability Scale (SUS), serta tes hasil belajar (pretest dan posttest). Hasil penelitian menunjukkan bahwa media dinyatakan layak, praktis, dan efektif meningkatkan hasil belajar siswa, serta model yang dikembangkan menunjukkan akurasi klasifikasi yang baik.
Kata kunci: media pembelajaran adaptif, machine learning, hasil belajar, matematika, Random Forest
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References
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.
Hapsari, D. I. S., & Fahmi, S. (2021). Pengembangan media pembelajaran interaktif berbasis android pada operasi pada matriks. FIBONACCI: Jurnal Pendidikan Matematika dan Matematika, 7(1), 9–10.
Huda, H. (2025). Pengembangan media pembelajaran interaktif berbasis AI untuk meningkatkan pemahaman konsep matematika siswa SD Negeri Kumbo. Jurnal Ilmiah Sultan Agung.
Kurnia, B. N., Marzal, J., & Zurwen. (2022). Pengembangan multimedia pembelajaran matematika model tutorial berbasis problem based learning pada materi aritmatika sosial untuk siswa SMP/MTs. Jurnal Cendekia: Jurnal Pendidikan Matematika, 6(1), 910–924.
Kurniawati, R. P., Hadi, F. R., & Rulviana, V. (2018). Pengembangan multimedia pembelajaran berbasis Cognitive Load Theory (CLT) pada materi volume kubus dan balok di sekolah dasar.
OECD. (2023). PISA 2022 results (Volume I): The state of learning and equity in education. OECD Publishing.
Sugiyono. (2019). Metode Penelitian Pendidikan (Kuantitatif, Kualitatif, dan R&D). Alfabeta.
VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197–221.
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