Tren Penelitian Deep Learning dalam Pendidikan Fisika: Analisis Bibliometrik dalam Satu Dekade Menggunakan Basis Data Scopus
DOI:
https://doi.org/10.37630/jpm.v15i4.3565Keywords:
Deep Learning, Pendidikan Fisika, Analisis BibliometrikAbstract
Perkembangan teknologi deep learning membuka peluang baru dalam pendidikan fisika untuk meningkatkan efektivitas pembelajaran, terutama dalam memahami konsep abstrak melalui simulasi virtual. Namun, pemanfaatannya masih terbatas dan belum ada pemetaan secara komprehensif yang berfokus pada pendidikan fisika. Penelitian ini bertujuan untuk menganalisis tren penelitian terkait deep learning dalam pendidikan fisika selama satu dekade terakhir. Metode analisis penelitian ini meliputi pertumbuhan publikasi dan sitasi tahunan, kontribusi penulis, afiliasi, dan negara, serta kata kunci dominan dan peluang topik penelitian. Prosedur penelitian ini dilakukan dengan analisis bibliometrik terhadap 90 dokumen terindeks scopus, dianalisis dengan Biblioshiny dan VOSviewer melalui tiga jenis visualisasi pada fitur co-occurence analysis. Hasil penelitian menunjukkan pertumbuhan publikasi yang terus meningkat signifikan, mengalami puncak publikasi terbanyak pada tahun 2023, dan kutipan terbesar pada tahun 2019, dengan rata-rata pertumbuhan tahunan mencapai 17,46%, kontribusi afiliasi institusi dari Universitas Michigan, dan negara produktif dari Amerika Serikat. Kata kunci paling dominan meliputi deep learning, students, learning systems, dan artificial intelligence. Kesimpulan dari penelitian ini menunjukkan tren arah penelitian menuju integrasi teknologi digital dan pedagogi inovatif, dengan peluang riset masa depan pada mobile learning, chat-gpt augmented reality, dan blended learning berbasis kecerdasan buatan.
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References
Adah, E. C., & Krajcik, J. S. (2019). Promoting deep learning through project-based learning: a design problem. Disciplinary and Interdisciplinary Science Education Research, 1(1). https://doi.org/10.1186/s43031-019-0009-6
Al Ka’bi, A. (2023). Proposed artificial intelligence algorithm and deep learning techniques for development of higher education. International Journal of Intelligent Networks, 4(November 2022), 68–73. https://doi.org/10.1016/j.ijin.2023.03.002
Alnasyan, B., Basheri, M., & Alassafi, M. (2024). The power of Deep Learning techniques for predicting student performance in Virtual Learning Environments: A systematic literature review. Computers and Education: Artificial Intelligence, 6(July), 100231. https://doi.org/10.1016/j.caeai.2024.100231
Ari Masitoh, P. N., Latifah, S., Saregar, A., Aziz, A., Suharto, & Jamaluddin, W. (2021). Bibliometric analysis of physics problem solving. IOP Conference Series: Earth and Environmental Science, 1796(1). https://doi.org/10.1088/1742-6596/1796/1/012009
Bao, L., & Koenig, K. (2019). Physics education research for 21st century learning. Disciplinary and Interdisciplinary Science Education Research, 1(1), 1–12. https://doi.org/10.1186/s43031-019-0007-8
Delen, I., Sen, N., Ozudogru, F., & Biasutti, M. (2024). Understanding the Growth of Artificial Intelligence in Educational Research through Bibliometric Analysis. Sustainability (Switzerland), 16(16). https://doi.org/10.3390/su16166724
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133(May), 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070
Hussain, S., Gaftandzhieva, S., Maniruzzaman, M., Doneva, R., & Muhsin, Z. F. (2021). Regression analysis of student academic performance using deep learning. Education and Information Technologies, 26(1), 783–798. https://doi.org/10.1007/s10639-020-10241-0
Jatmiko, B., Prahani, B. K., Suprapto, N., Admoko, S., Deta, U. A., Lestari, N. A., Jauhariyah, M. N. R., Yantidewi, M., & Muliyati, D. (2021). Bibliometric analysis on online physics learning during COVID-19 Pandemic: Contribution to physics education undergraduate program. In D. null, Z. A., P. B.K., P. N.P., L. N.A., & D. U.A. (Eds.), Journal of Physics: Conference Series (Vol. 2110, Issue 1). Institute of Physics. https://doi.org/10.1088/1742-6596/2110/1/012018
Jiang, W. (2025). Deep Learning-Based Prediction of Student Performance in Physics Education Using Multimodal Data. Proceedings of the 2025 International Conference on Big Data and Informatization Education, 119–124. https://doi.org/10.1145/3729605.3729627
Lakka, I., Zafeiropoulos, V., & Leisos, A. (2023). Online Virtual Reality-Based vs. Face-to-Face Physics Laboratory: A Case Study in Distance Learning Science Curriculum. Education Sciences, 13(11). https://doi.org/10.3390/educsci13111083
Lin, Y., Chen, H., Xia, W., Lin, F., Wang, Z., & Liu, Y. (2025). A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining. Data Science and Engineering. https://doi.org/10.1007/s41019-025-00303-z
Lintangesukmanjaya, R. T., Dwikoranto, n., Awwalina, D. P., Setiani, R., & Bergsma, L. N. (2025). Potential study SDGs 4 of deep learning approaches to improve problem solving with machine learning inovation: Empirical and bibliometric analysis. In N. Suprapto, B. K. Prahani, S. Andari, M. A. Ghofur, & M. Satriawan (Eds.), E3S Web of Conferences (Vol. 640). EDP Sciences. https://doi.org/10.1051/e3sconf/202564002018
Mahligawati, F., Allanas, E., Butarbutar, M. H., & Nordin, N. A. N. (2023). Artificial intelligence in Physics Education: A comprehensive literature review. Journal of Physics: Conference Series, 2596(1). https://doi.org/10.1088/1742-6596/2596/1/012080
Nurjanah, S., Martaputri, N. A., & Suardi, I. K. (2024). Jurnal Pendidikan Fisika Artificial Intelligence in Physics Education Research in Two Decades : A Bibliometric Study from Scopus Database. Jurnal Pendidikan Fisika, 12(2), 53–86. https://doi.org/10.26618/jpf.v12i2.14745
Prahani, B. K., Saphira, H. V., Wibowo, F. C., & Bunyamin, M. A. H. (2024). Mapping research on integrating artificial intelligence into physics learning. Perspektivy Nauki i Obrazovania, 71(5), 305–317. https://doi.org/10.32744/pse.2024.5.18
Pratama, F. R., Santoso, H. B., Junus, K., Michael, J., Mannix, I. A., & Athaya, H. (2025). Constructivism in Online and Hybrid Learning Before and After Covid-19: a Systematic Literature Review. Jurnal Eduscience, 12(4), 1111–1129. https://doi.org/10.36987/jes.v12i4.7314
Sofa, E. L., Saptanigrum, E., Khoiri, N., & Kurniawan, A. F. (2025). Effectiveness of Integrating Deep Learning into Problem- Based Learning with PhET Simulations to Enhance Students ’. Jurnal Pendidikan Fisika, 13(3), 416–428. https://doi.org/10.26618/ttrea958
Sumarni, R. A., & Yona Okyranida, I. (2025). Deep Learning in Physics Education: Exploring the Potential of Mindful, Meaningful, and Joyful for a Better Learning Experience. Navigation Physics: Journal of Physics Education, 7(1).
Turmuzi, M., & Tyaningsih, R. Y. (2025). A Bibliometric Analysis of the Development of Artificial Intelligence (AI) Research in Education in Scopus Indexed Journals: What are the Future Trends of this Research? TEM Journal, 14(1), 671–683. https://doi.org/10.18421/TEM141-60
Wang, S., Wang, F., Zhu, Z., Wang, J., Tran, T., & Du, Z. (2024). Artificial intelligence in education: A systematic literature review. Expert Systems with Applications, 252(PA), 124167. https://doi.org/10.1016/j.eswa.2024.124167
Wati, W., Amriyah, C., & Astuti, T. W. (2019). Pengembangan Modul Fisika Berdasarkan Hambatan Belajar Siswa. Indonesian Journal of Science and Mathematics Education, 2(1), 107–115. https://doi.org/10.24042/ijsme.v2i1.3977
Yaseen, H., Mohammad, A. S., Ashal, N., Abusaimeh, H., Ali, A., & Sharabati, A. A. A. (2025). The Impact of Adaptive Learning Technologies, Personalized Feedback, and Interactive AI Tools on Student Engagement: The Moderating Role of Digital Literacy. Sustainability (Switzerland), 17(3), 1–27. https://doi.org/10.3390/su17031133
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