Penerapan Hybrid C4.5 dan Naïve Bayes untuk Rekomendasi Jurusan Kuliah pada Siswa SMA - Lubis, Tiara; Riza, Ferdy (Universitas Multi Data Palembang, 2025)
This research aims to design and build a web-based college major recommendation system for high school students focused on SMA Negeri 8 Medan using a hybrid C4.5–Naïve Bayes approach. The C4.5 algorithm is used for feature selection from report card grades and interest test results, while Naïve Bayes classifies majors based on the selected features. This research uses a quantitative approach because the main focus of this approach is on the analysis of numerical data generated by the model. Specifically, the research will measure evaluation metrics such as accuracy, precision, recall, and F1-score to objectively assess how effective the system is in providing major recommendations to students at SMA Negeri 8 Medan. The results of this research are that a web-based system has been successfully designed and developed using a hybrid approach of the C4.5 and Naïve Bayes algorithms where C45 is used in the feature selection stage to determine the most influential attributes (grades and interests) in determining majors, and the Naïve Bayes algorithm is used as the main classification model to predict majors based on the selected features.