Systematic Literature Review: Tren dan Tantangan Machine Learning pada Sistem Rekomendasi Kursus Daring
DOI:
https://doi.org/10.52158/ct1kr873Keywords:
machine learning, course recommendation, deep learning, SLR, e-learningAbstract
This study presents a Systematic Literature Review (SLR) on the application of machine learning in online course recommendation systems. The aim is to map research trends, methodological approaches, and challenges in developing AI-based recommendation systems for online education. A total of 40 Scopus-indexed articles published between 2020 and 2025 were analyzed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, using the Watase UAKE tool for literature selection. The findings reveal that deep learning and hybrid models are the most dominant approaches, with a significant increase observed during 2022–2024. China contributes 57.5% of the studies, followed by India and Taiwan, indicating a strong research concentration in Asia. Combined architectures such as CNN–LSTM–ResNet achieved the highest accuracy (99.2%), while Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) are emerging as adaptive approaches for course recommendation. The main challenges identified include real-time adaptability, computational efficiency, and model transparency. The main contribution of this paper is to provide a comprehensive map of current research and outline future directions toward adaptive, efficient, and explainable online course recommendation systems.T
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