ANALISIS PENERAPAN DEEP LEARNING DALAM PEMBELAJARAN DI SEKOLAH DASAR: STUDI EMPIRIS DI KOTA MATARAM
Main Article Content
Zainul Muttaqin
Erwin Hadi
Hapipi
Usman Jayadi
This study aims to analyze the application of deep learning technology in primary school education in Mataram City. Deep learning, as a branch of artificial intelligence (AI), has the potential to enhance learning effectiveness through personalized teaching materials and in-depth data analysis regarding students' development. The study focuses on four primary schools in Mataram City, namely SDN 45 Ampenan, SDN 18 Cakranegara, SDN 24 Cakranegara, and SDN 22 Mataram. The method used is a case study with a qualitative approach, involving interviews with teachers and school principals, as well as direct observations of the implementation of deep learning in the learning process. The results show that, although deep learning technology is still applied on a limited scale, its impact on improving the quality of learning, such as increased student motivation, better understanding of the material, and more accurate analysis of student development, is quite significant. However, the implementation of this technology also faces various challenges, particularly related to infrastructure limitations and the need for ongoing teacher training. This study recommends the need for improved infrastructure support and continuous training to maximize the potential of deep learning in primary education.
Alonso, S. (2017). Deep Learning in Education: Applications and Challenges. Journal of Educational Technology, 38(2), 45-59.
Anderson, C., & Bowers, A. (2017). Artificial Intelligence in Education: A Review. International Journal of Educational Technology, 18(5), 23-34.
Arifudin, O. . (2025). Why Digital Learning is the Key to the Future of Education. International Journal of Education and Digital Learning (IJEDL), 3(4), 201–210. https://doi.org/10.47353/ijedl.v3i4.261
Baker, R. S. (2016). Big data and education. Teachers College Record, 118(5), 1-20.
Castelli, D., & Rios, L. (2018). Applications of machine learning in educational settings. Computers in Human Behavior, 88, 71-80.
Chen, G., Lee, H., & Hwang, G. (2021). A comprehensive review of AI applications in education. Computers & Education, 172, 104246.
Chou, P. N., & Wang, H. L. (2017). Teaching machine learning in the classroom: A review of the state of the art. Journal of Educational Computing Research, 55(2), 237-254.
Dastjerdi, A., & Torkashvand, M. (2019). A review on applications of deep learning in education. Educational Technology Research and Development, 67(6), 1389-1406.
Gray, R., & Vaughan, P. (2019). Data-driven personalized learning: The role of artificial intelligence in educational technology. Journal of Educational Computing Research, 57(3), 725-739.
He, J., Chen, Z., & Zhang, L. (2019). Adaptive Learning Systems: Deep Learning in Education. Computers & Education, 133, 31-40.
Huang, Z., & Cheng, X. (2020). Challenges and solutions for implementing AI in education. Educational Technology Research and Development, 68(4), 1209-1226.
Hushin, H. . (2025). Increasing Global Access to Education with Digital Technology. International Journal of Education and Digital Learning (IJEDL), 3(4), 167–176. https://doi.org/10.47353/ijedl.v3i4.259
Ismail, S. ., & Ling, Z. . (2025). Digital Learning: A Solution for More Inclusive and Affordable Education. International Journal of Education and Digital Learning (IJEDL), 3(4), 191–200. https://doi.org/10.47353/ijedl.v3i4.260
Kotsiantis, S. B., & Pintelas, P. E. (2017). Machine learning techniques in education: A review. Artificial Intelligence Review, 48(1), 1-22.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Mahmadov, Y. . (2025). Transforming Education Through Digital Learning: Embracing the New Era of Learning. International Journal of Education and Digital Learning (IJEDL), 3(4), 157–166. https://doi.org/10.47353/ijedl.v3i4.258
Rauf, M. A., & Ahmad, A. (2020). Review on the implementation of machine learning in educational technologies. Computers & Education, 148, 103787.
Zhang, Y., Liu, H., & Xu, F. (2020). Predicting student performance using deep learning algorithms. Journal of Artificial Intelligence in Education, 30(4), 381-394.
Zhou, X., Zhang, T., & Li, H. (2019). The effectiveness of adaptive learning systems: A systematic review. Educational Technology Research and Development, 67(3), 601-623.