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Abstract

Pemahaman konsepsi yang mendalam merupakan pilar utama dalam pembelajaran fisika, namun kesulitan utama terletak pada identifikasi dan diagnosis pola miskonsepsi siswa yang seringkali terhambat oleh metode evaluasi tradisional. Penelitian observasi sistematis ini bertujuan untuk mengidentifikasi dan mensintesis secara komprehensif literatur mengenai penerapan serta menyebarkan efektivitas Algoritma Pohon Keputusan (Decision Tree Algorithm) dalam memodelkan dan mengklasifikasikan tingkat pemahaman siswa. Kajian ini menggunakan metode Systematic Literature Review (SLR) dengan kerangka PRISMA, mengumpulkan artikel empiris dari basis data bereputasi seperti SINTA, Scopus, dan ERIC yang terbit dalam rentang tahun 2017–2024. Hasil observasi menunjukkan bahwa Algoritma Pohon Keputusan (termasuk variasi Random Forest) dimanfaatkan secara luas sebagai alat diagnostik prediktif untuk membedakan kategori pemahaman dan mendeteksi miskonsepsi, dengan laporan akurasi klasifikasi model yang secara konsisten melebihi 80%. Simpulannya, pemodelan berbasis Pohon Keputusan merupakan alat berbasis data yang sangat efektif, mampu menyediakan aturan keputusan yang terstruktur dan dapat ditindaklanjuti, sehingga memiliki kontribusi signifikan dalam mendukung perancangan intervensi pedagogis yang adaptif dan terpersonalisasi dalam konteks pendidikan fisika.

Keywords

Algoritma Pohon Keputusan Pemahaman Fisika Systematic Review Diagnostik Miskonsepsi Machine Learning

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite
Mugono, A. Y., & Amnie, E. (2026). Pemodelan Pemahaman Siswa dalam Fisika Menggunakan Algoritma Pohon Keputusan: A Systematic Review. PSEJ (Pancasakti Science Education Journal), 11(1), 29-38. https://doi.org/10.24905/psej.v11i1.287

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