Khairi Budayawan, Ganefri Ganefri, Muhammad Anwar, Agariadne Dwinggo Samala, Natalie-Jane Howard, Randi Proska Sandra
This study explores the application of the Naïve Bayes algorithm within the framework of Project-Based Learning (PjBL) to predict student graduation timing and likelihood. The evaluation of student competencies across several performance dimensions, such as problem analysis, project planning, data preparation, feature extraction, and algorithm implementation, demonstrates the effectiveness of the approach. Predictive analysis and result interpretation were successful, indicating a strong correlation between project-based learning outcomes and graduation success. Additionally, the research uncovers insights into the role of creativity and innovation in predicting student graduation. This study highlights the potential benefits of integrating PjBL into educational curricula and underscores the utility of the Naïve Bayes method in forecasting graduation outcomes in higher education. © 2025 Khairi Budayawan et al.; published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License. The article is published with Open Access at https://www.temjournal.com/
Faculty of Engineering, Universitas Negeri Padang, Padang, Indonesia; Centre for Higher Education Research and Evaluation, Lancaster University, Lancaster, United Kingdom
