ANALISIS BIBLIOMETRIK DAN SLR: TREN PENERAPAN JST DALAM PENILAIAN PEMBELAJARAN FISIKA BERBASIS VIRTUAL LABORATORY
Abstract
Sistem penilaian pembelajaran fisika masih didominasi oleh pendekatan konvensional yang cenderung subjektif dan belum mampu mengakomodasi analisis data pembelajaran yang kompleks. Perkembangan teknologi, khususnya virtual laboratory, menghasilkan data interaksi belajar yang berpotensi dimanfaatkan untuk meningkatkan kualitas penilaian, namun pemanfaatannya masih terbatas. Penelitian ini bertujuan untuk mengidentifikasi tren penelitian, menganalisis perkembangan penerapan jaringan syaraf tiruan (JST), serta memetakan peluang pengembangan sistem penilaian pembelajaran berbasis virtual laboratory pada periode 2016–2026. Metode yang digunakan adalah Systematic Literature Review (SLR) yang dikombinasikan dengan analisis bibliometrik. Data diperoleh dari database Scopus dengan 343 artikel yang teridentifikasi pada periode 2016–2026, kemudian disaring menjadi 143 artikel berdasarkan kriteria tertentu, dan diperoleh 20 artikel akhir untuk dianalisis lebih lanjut. Hasil analisis menunjukkan bahwa pendekatan machine learning, khususnya JST, banyak digunakan dalam prediksi dan evaluasi hasil belajar siswa dengan tingkat akurasi yang tinggi. Namun, sebagian besar penelitian masih berfokus pada hasil akhir pembelajaran dan belum mengintegrasikan data proses belajar secara menyeluruh. Selain itu, integrasi antara JST dan virtual laboratory dalam sistem penilaian pembelajaran masih sangat terbatas. Penelitian ini menegaskan bahwa pengembangan sistem penilaian berbasis JST yang terintegrasi dengan virtual laboratory berpotensi menghasilkan evaluasi pembelajaran yang lebih komprehensif, adaptif, dan berbasis data dalam pembelajaran fisika.
Full Text:
PDF (Indonesian)References
Cheng, W. (2026). Prediction and Classification of Student Performance Through an Ensemble Machine Learning Approach. Journal of The Institution of Engineers (India): Series B, 107(B), 297–307. https://doi.org/https://doi.org/10.1007/s40031-025-01240-1
Çınar, A., Ince, E., Gezer, M., & Yılmaz, Ö. (2020). Machine learning algorithm for grading open-ended physics questions in Turkish. Education and Information Technologies, 25, 3821–3844. https://doi.org/https://doi.org/10.1007/s10639-020-10128-0
Darman, D. R., Suhandi, A., Kaniawati, I., Samsudin, A., & Wibowo, F. C. (2024). Virtual laboratory in physics education: A systematic review. ELECTRONIC PHYSICS INFORMATICS INTERNATIONAL CONFERENCE (EPIIC). https://doi.org/https://doi.org/10.1063/5.0210640
Handayani, K. S., Lumbanbatu, K., & Simanjuntak, M. (2021). Jaringan Syaraf Tiruan dengan Algoritma Backpropagation untuk Memprediksi Nilai Ujian Kompetensi Siswa (Studi Kasus Smks Jabal Rahmah Stabat). Jurnal Ilmiah Abdi Ilmu, 14(1), 73–80.
Hariharasitaraman, S., Jayavel, A., Gnanasekaran, S., Sohail, S. S., & Madsen, D. Ø. (2025). Interpretable and ethical learning assessment transformer ( IELAT ): an explainable transformer model for personalized student assessments. Cogent Education, 12(1). https://doi.org/10.1080/2331186X.2025.2485508
Javed, M. S., Aslam, M., & Khurshid, S. K. (2025). An Intelligent Model for Parametric Cognitive Assessment of E-Learning-Based Students. Information, 16(2), 1–35. https://doi.org/https://doi.org/10.3390/info16020093
Kaur, S., Goel, D., Ramachandaran, D. S., Devi, S., Yadav, B., & Goyal, A. (2025). AI-Powered and Mobile-Integrated Assessment Models Using Random Forest: Redefining Examminations and Grading. International Journal of Interactive Mobile Technologies, 19(14), 57–69.
Lau, E. T., Sun, L., & Yang, Q. (2019). Modelling, prediction and classification of student academic performance using artificial neural networks. SN Applied Sciences, 1(9), 1–10. https://doi.org/10.1007/s42452-019-0884-7
Musahir, M. (2022). Penggunaan media pembelajaran fisika dengan virtual laboratory berbasis “PhET Interactive Simulations” untuk meningkatkan kemampuan penguasaan konsep kognitif rangkaian resistor. Compton: Jurnal Ilmiah Pendidikan Fisika, 9(2), 122–130. https://doi.org/10.30738/cjipf.v9i2.13664
N. Kapita, S., Mahdi, S., & Tempola, F. (2020). Penilaian Pengetahuan Siswa Dengan Jaringan Syaraf Tiruan Algoritma Perceptron. Techno: Jurnal Penelitian, 9(1), 372. https://doi.org/10.33387/tjp.v9i1.1712
Nurfadila, Z., & Arlita, W. (2025). Penerapan Laboratorium Virtual dalam Pembelajaran Fisika di Era Digital. Jurnal Inovasi Pendidikan Dan Pengajaran (JIPP), 4(2), 33–36. https://doi.org/10.31571/jipp.v4i2.9317
Öz, E., Bulut, O., Cellat, Z. F., & Yürekli, H. (2024). Stacking: An Ensemble Learning Approach to Predict Student Performance in PISA 2022. Education and Information Technologies, 30, 7753–7779. https://doi.org/https://doi.org/10.1007/s10639-024-13110-2
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Thebmj, 372. https://doi.org/10.1136/bmj.n71
Santoso, P. H., Santosa, H. S., Istiyono, E., Haryanto, H., & Retnawati, H. (2023). Predicting Physics Students’ Achievement Using In-Class Assessment Data: A Comparison of Two Machine Learning Models. Physics Education Research Journal, 5(2), 97–106. https://doi.org/10.21580/perj.2023.5.2.14217
Tedja, B., Al Musadieq, M., Kusumawati, A., & Yulianto, E. (2024). Systematic literature review using PRISMA: exploring the influence of service quality and perceived value on satisfaction and intention to continue relationship. Future Business Journal, 10(1), 1–9. https://doi.org/10.1186/s43093-024-00326-4
Thavasi, S., & Revathi, T. (2025). A personalized machine learning–based system to evaluate students’ skillset and analyze the gap between academia and industry for engineering students. Kybernetes: The International Journal of Cybernetics, Systems, and Management Sciences, 54(9), 4850–4865. https://doi.org/https://doi.org/10.1108/K-01-2024-0053
Tin Tin, T., Hock, L. S., & Ikumapayi, O. M. (2024). Educational Big Data Mining : Comparison of Multiple Machine Learning Algorithms in Predictive Modelling of Student Academic Performance. (IJACSA) International Journal of Advanced Computer Science and Applications, 15(6), 633–645. https://doi.org/https://dx.doi.org/10.14569/IJACSA.2024.0150664
Vaswani, A. (2017). Attention Is All You Need. 31st Conference on Neural Information Processing Systems (NIPS 2017), Nips.
Wang, Y., Zhang, L., & Pang, M. (2024). Virtual experiments in physics education: a systematic literature review. Research in Science & Technological Education, 43(2), 633–655. https://doi.org/https://doi.org/10.1080/02635143.2024.2327995
Xuan Lam, P., Quoc Hung Mai, P., Hung Nguyen, Q., & Pham, T. (2024). Enhancing educational evaluation through predictive student assessment modeling. Computers and Education: Artificial Intelligence, 6. https://doi.org/10.1016/j.caeai.2024.100244
Yang, K. (2024). Predicting Student Performance Using Artificial Neural Network Analysis. Journal of Arts, Society, and Education, 42(5), 516–517. https://doi.org/10.1111/j.1365-2923.2008.03052.x
You, H., Hong, M., Zhu, L., & Zhenhan, F. (2025). Machine Learning Approaches for Predicting U.S. Students’ Scientific Literacy: An Analysis of Key Factors Across Performance Levels and Socioeconomic Statuses. International Journal of Science and Mathematics Education, 23, 2755–2783. https://doi.org/10.1007/s10763-025-10545-y
Zola, F., Nurcahyo, G. W., & Santony, J. (2018). Jaringan Syaraf Tiruan Menggunakan Algoritma Backpropagation Untuk Memprediksi Prestasi Siswa. Jurnal Teknologi Dan Open Source, 1(1), 58–72.
DOI: http://dx.doi.org/10.30821/fisitekfisitek.v10i1.31244
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Prodi Fisika - Universitas Islam Negeri Sumatera Utara
Website : https://fisika.uinsu.ac.id/