PENERAPAN METODE DECISION TREE DALAM PENENTUAN JURUSAN SISWA - Nurul Aini, Yuli; Faqih, Ahmad; Dwilestari, Gifthera (JURNAL ILMIAH INFORMATIKA, 2025)
Choosing a major for vocational students is an important step in determining their future career. Inappropriate decisions in choosing a major can have a negative impact on the learning process and career prospects of students. This research aims to apply the Decision Tree C4.5 algorithm in determining student majors based on historical data from SMK Plus Qurrota A'yun. The main attributes used in this model include gender, practical score, report card score, and rank. The data analysis process is carried out using KDD (Knowledge Discovery in Databases) stages which include preprocessing (normalization and labeling), data transformation with SMOTE to balance the data, and dataset division (80% for training and 20% for testing). The results of the C4.5 Decision Tree model show an accuracy rate of 86.59%, with report card and practical grades as the most significant attributes in determining student majors. Evaluation of the model showed variation in performance between majors, with high precision in TSM (92.22%) and weak recall in TKJ (73.91%). Overall, the algorithm proved to be effective for providing major recommendations based on historical data, although optimization is needed for majors with lower precision and recall values.