Evolusi, Tren dan Arah Penelitian tentang Model Deep Learning: Analisis Bibliometrik

Maximus Tamur

doi:10.24014/sjme.v11i1.36368

Evolusi, Tren dan Arah Penelitian tentang Model Deep Learning: Analisis Bibliometrik

Maximus Tamur

Abstract

Keadaan literatur yang berkembang saat ini menyajikan bahwa proses pembelajaran berkualitas tinggi didasarkan pada model pembelajaran mendalam (Deep Learning Model/DLM). Meskipun DLM telah diterapkan secara luas, hanya sedikit penelitian yang menyoroti evolusi atau lintasan pertumbuhan, tren, dan juga arah penelitian di masa depan. Penelitian ini mengidentifikasi penelitian tentang DLM yang dapat memberikan perspektif global tentang pengembangan dan penyelidikan lebih lanjut. Tujuan tersebut dicapai dengan menganalisis 308 artikel jurnal dan prosiding dari tahun 2013 hingga 2025 menggunakan basis data Google Scholar dan Scopus. Pencarian data menggunakan aplikasi Publish or Perish (POP), dan program penampil VOS membantu dalam menganalisis hubungan antar tema. Penelitian ini membahas dua masalah: (i) meninjau lintasan pertumbuhan studi terkait DLM; dan (ii) menentukan pemetaan antartema untuk mengidentifikasi kesenjangan dan topik yang paling penting. Hasil analisis menunjukkan bahwa lintasan studi tampak cair dan dimediasi oleh dampak pembatasan sosial karena Covid-19. Topik utama dan kesenjangan penelitian dibahas. Beberapa implikasi disajikan sebagai informasi yang bermanfaat bagi para ilmuwan dan pemangku kepentingan.

Keywords

Analisis Bibliometrik; Deep Learning; Google Scholar dan Scopus

Full Text:

PDF

References

Abbasimehr, H., Paki, R., & Bahrini, A. (2022). A Novel Approach Based on Combining Deep Learning Models with Statistical Methods for COVID-19 Time Series Forecasting. In Neural Computing and Applications. Springer. https://doi.org/10.1007/s00521-021-06548-9

Baumgartl, H. (2020). A Deep Learning-Based Model for Defect Detection in Laser-Powder Bed Fusion Using in-Situ Thermographic Monitoring. Progress in Additive Manufacturing, 5(3), 277–285. https://doi.org/10.1007/s40964-019-00108-3

Cvitić, I., Peraković, D., Periša, M., & Gupta, B. (2021). Ensemble Machine Learning Approach for Cassification of IoT Devices in Smart Home. International Journal of Machine Learning and Cybernetics, 12(11), 3179–3202. https://doi.org/10.1007/s13042-020-01241-0

Danesh, M., Gharehbaghi, A., Mehdizadeh, S., & Danesh, A. (2024). A Comparative Assessment of Machine Learning and Deep Learning Models for the Daily River Streamflow Forecasting. Water Resources Management, 12(5), 1–14. https://doi.org/10.1007/s11269-024-04052-y

Das, A. K., Das, S., & Mukherjee, J. (2021). Largest Triangle inside a Terrain. Theoretical Computer Science, 858, 90–99. https://doi.org/10.1016/j.tcs.2020.12.018

Dong, A. (2023). Analysis on the Steps of Physical Education Teaching Based on Deep Learning. International Journal of Distributed Systems and Technologies, 14(2), 1–15. https://doi.org/10.4018/IJDST.317937

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(April), 285–296. https://doi.org/10.1016/j.jbusres.2021.04.070

Dutta, S. (2018). Classification of Diabetic Retinopathy Images by Using Deep Learning Models. International Journal of Grid and Distributed Computing, 11(1), 89–106. https://doi.org/10.14257/ijgdc.2018.11.1.09

Ferentinos, K. P. (2018). Deep Learning Models for Plant Disease Detection and Diagnosis. Computers and Electronics in Agriculture, 145(7), 311–318. https://doi.org/10.1016/j.compag.2018.01.009

Grossberg, S. (2020). A Path Toward Explainable AI and Autonomous Adaptive Intelligence : Deep Learning, Adaptive Resonance, and Models of Perception, Emotion, and Action. Frontiers In, 14(June), 1–26. https://doi.org/10.3389/fnbot.2020.00036

Hallinger, P., & Chatpinyakoop, C. (2019). A Bibliometric Review of Research on Higher Education for Sustainable Development, 1998-2018. Sustainability (Switzerland), 11(8). https://doi.org/10.3390/su11082401

Hallinger, P., & Nguyen, V. T. (2020). Mapping the Landscape and Structure of Research on Education for Sustainable Development: A Bibliometric Review. Sustainability (Switzerland), 12(5), 1–16. https://doi.org/10.3390/su12051947

Han, S., Yoon, S., & Euom, I. (2024). The Machine Learning Ensemble for Analyzing Internet of Things Networks : Botnet Detection and Device Identification. Computer Modeling in Engineering & Sciences, 141(2), 1495–1518. https://doi.org/10.32604/cmes.2024.053457

Hassan, M. M. (2020). A Hybrid Deep Learning Model for Efficient Intrusion Detection in Big Data Environment. Information Sciences, 513, 386–396. https://doi.org/10.1016/j.ins.2019.10.069

Iizuka, O. (2020). Deep Learning Models for Histopathological Classification of Gastric and Colonic Epithelial Tumours. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-58467-9

Juandi, D., Kusumah, Y. S., Tamur, M., Perbowo, K. S., & Wijaya, T. T. (2021). A Meta-Analysis of Geogebra Software Decade of Assisted Mathematics Learning : What to Learn and Where to Go? Heliyon, 7(5), e06953. https://doi.org/10.1016/j.heliyon.2021.e06953

Khan, F. A. (2019). TSDL: A Two-Stage Deep Learning Model for Efficient Network Intrusion Detection. IEEE Access, 7, 30373–30385. https://doi.org/10.1109/ACCESS.2019. 2899721

Koch, S. (2019). ABC: A Big Cad Model Dataset for Geometric Deep Learning. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Vol. 2019, pp. 9593–9603). https://doi.org/10.1109/CVPR.2019.00983

Li, Q., & Kim, J. (2021). A Deep Learning-Based Course Recommender System for Sustainable Development in Education. Applied Sciences, 11(19), 1–18. https://doi.org/10.3390/app11198993

Liu, Y. (2020). Bibliometric Analysis of Research on Soil Health from 1999 to 2018. Journal of Soils and Sediments, 20(3), 1513–1525. https://doi.org/10.1007/s11368-019-02519-9

Livieris, I. E., Stavroyiannis, S., Pintelas, E., & ... (2020). A Novel Validation Framework to Enhance Deep Learning Models in Time-Series Forecasting. Neural Computing and …. https://doi.org/10.1007/s00521-020-05169-y

Loey, M. (2021). A Hybrid Deep Transfer Learning Model with Machine Learning Methods for Face Mask Detection in the Era of the COVID-19 Pandemic. Measurement: Journal of the International Measurement Confederation, 167. https://doi.org/10.1016/ j.measurement.2020.108288

Mehtab, S., & Sen, J. (2020). A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models. International Journal of Business …, 7(3), 1–13. https://doi.org/10.1504/IJBFMI.2020.115691

Phan, T. T., Do, T. T., Trinh, T. H., Tran, T., Doung, H. T., Trinh, T. P. T., Do, B. C., & Nguyen, T.-T. (2021). A Bibliometric Review on Realistic Mathematics Education in Scopus Database Between 1972-2019. European Journal of Educational Research, 11(2), 1133–1149.

Rai, H. M., & Yoo, J. (2023). A Comprehensive Analysis of Recent Advancements in Cancer Detection Using Machine Learning and Deep Learning Models for Improved Diagnostics. Journal of Cancer Research and Clinical Oncology. https://doi.org/10.1007/s00432-023-05216-w

Saputra, N. A., Hamidah, I., & Setiawan, A. (2023). a Bibliometric Analysis of Deep Learning for Education Research. Journal of Engineering Science and Technology, 18(2), 1258–1276.

Shah, M., & Sureja, N. (2025). A Comprehensive Review of Bias in Deep Learning Models: Methods, Impacts, and Future Directions. Archives of Computational Methods in Engineering. https://doi.org/10.1007/s11831-024-10134-2

Tamur, M., Juandi, D., & Subaryo. (2023). A Meta-Analysis of the Implementation of the Gamification Approach of the Last Decade. AIP Conference Proceedings, 090002(1), 1–7. https://doi.org/10.1063/5.0155519

Tamur, M., Wijaya, T. T., Makur, A. P., Wibisono, Y., & Pantaleon, K. V. (2024). The Global Trend of Augmented Reality-Based Learning and Its Impact on Students’ Academic Ability : A Meta-Analysis. HIPOTENUSA: Journal of Mathematical Society, 6(2), 216–233. https://doi.org/10.18326/hipotenusa.v6i2.2454

Tatar, E., Berrin, T., Izmanli, K. A. Ğ., & Akkaya, A. (2014). The Effect of a Dynamic Software on the Success of Analytical Analysis of the Circle and Prospective Mathematics Teachers Opinions. Journal of Electronic Science and Mathematics Education, 8(1), 153–177. https://doi.org/10.12973/nefmed.2014.8.1.a7

Timmers, C. F., Broek, J. B. Den, & Berg, S. M. Van Den. (2013). Motivational Beliefs, Student Effort, and Feedback Behaviour in Computer-Based Formative Assessment. Computers & Education, 60(1), 25–31. https://doi.org/10.1016/j.compedu.2012.07.007

Trivedi, J., & Shah, M. (2024). A Systematic and Comprehensive Study on Machine Learning and Deep Learning Models in Web Traffic Prediction. Archives of Computational Methods in Engineering, 9(1), 1–12. https://doi.org/10.1007/s11831-024-10077-8

Wang, S. (2017). Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model. PLoS Computational Biology, 13(1). https://doi.org/10.1371/journal.pcbi.1005324

Xiao, Y. (2018). A Deep Learning-Based Multi-Model Ensemble Method for Cancer Prediction. Computer Methods and Programs in Biomedicine, 153, 1–9. https://doi.org/10.1016/j.cmpb.2017.09.005

Zhao, X. (2018). A Deep Learning Model Integrating FCNNs and CRFs for Brain Tumor Segmentation. Medical Image Analysis, 43, 98–111. https://doi.org/10.1016/j.media.2017.10.002

Zupic, I., & Čater, T. (2015). Bibliometric Methods in Management and Organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629

DOI: http://dx.doi.org/10.24014/sjme.v11i1.36368