Sistem Rekomendasi Belajar Mandiri Menggunakan Generative AI Berbahasa Indonesia untuk Mahasiswa Informatika - Rani Robetty Saragih (Asosiasi Riset Teknik Elektro dan Informatika Indonesia, 2025)
The need for adaptive and personalized learning systems is increasing along with the advancement of information technology. Informatics students face challenges in choosing materials that suit their abilities and interests. This research aims to design and evaluate an Generative AI-based independent learning recommendation system in Indonesian that can help students navigate the learning process more efficiently. The system utilizes a generative language model to provide relevant learning material suggestions based on students' individual learning history and preferences. This study uses a software engineering approach and experimental evaluation. The dataset is collected from various reference sources of learning and user interaction with the system. The Generative AI model is trained to generate contextual and Indonesian-language content recommendations. The results of the evaluation show that this system is able to increase the effectiveness and satisfaction of students' independent learning. These findings confirm the great potential of generative AI technology in supporting higher education, particularly in supporting personalized learning in local languages.