Analisis Metode Decision Tree dan Regresi Logistik Sebagai Sistem Rekomendasi Kenaikan Golongan Berdasarkan Kinerja Pegawai pada Universitas Lamappapoleonro - Aksa, Andi Nurul; Achmad, Andani; Arda, Abdul Latief (Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen, 2025)
This research focuses on the importance of employee performance in supporting organizational success, especially in the promotion process at Lamappapoleonro University which is still done manually. Therefore, this research aims to develop a recommendation system for promotion using the Decision Tree and Logistic Regression methods, which is expected to speed up and simplify the decision-making process regarding employee promotions. The Decision Tree algorithm is used to classify employee performance in the form of sufficient, good, and excellent variables, while the Logistic Regression algorithm is used to predict the feasibility of employee promotion with the variable feasible or inappropriate. The data used in this study includes 12 independent variables, such as attendance, discipline, responsibility, and innovative ability. The analysis results show that the Decision Tree and Logistic Regression methods are able to produce accurate predictions, with an accuracy rate of 91.67% and 100% respectively. The main factors that influence promotion are honesty, discipline, and innovation ability. With this recommendation system, the employee promotion process becomes more efficient and accurate, providing significant benefits for human resource management at Lamappapoleonro University.