A Prediksi Rekomendasi Pemilihan Kejuruan pada Sekolah Menengah Kejuruan Menggunakan Perbandingan Metode Decision Tree C4.5 dan Naïve Bayes - Windari, Ratih; Nugroho, Handoyo Widi (Building of Informatics, Technology and Science (BITS), 2025)
SMK Negeri 4 Bandar Lampung faces challenges in assisting students in selecting a major that aligns with their potential, interests, and abilities. The decision-making process for choosing a major is often influenced by subjective factors that lack transparency and may not be entirely accurate. Therefore, a system is needed to provide more accurate and objective recommendations. This study develops a predictive system for major selection at SMK Negeri 4 Bandar Lampung using two methods: the Decision Tree C4.5 algorithm and the Naïve Bayes algorithm. The system utilizes seven key attributes as predictive variables, including mathematics scores, English scores, science (IPA) scores, Indonesian language scores, academic achievements, participation in extracurricular activities, and color blindness condition. The study findings indicate that the C4.5 algorithm achieves an accuracy of 84.46%, whereas the Naïve Bayes algorithm outperforms it with an accuracy of 92.23%. This suggests that the Naïve Bayes algorithm is more effective for this application. Nevertheless, both methods still have limitations that can be improved through parameter optimization and more in-depth data processing. The implementation of this data-driven system is expected to enhance the efficiency of providing more relevant major recommendations at SMK Negeri 4 Bandar Lampung and serve as an inspiration for other schools to adopt similar approaches to improve education quality.