Implementasi Algoritma Naive Bayes dalam Penentuan Karyawan Terbaik - Wulan Dari; Dian Mayasari (Asosiasi Riset Ilmu Matematika dan Sains Indonesia, 2026)
Human resources (employees) are essential assets that determine the sustainability and development of a company. Employee performance evaluation processes for selecting the best employees often encounter challenges due to manual procedures, subjectivity, and lengthy assessment times. Therefore, an objective, accurate, and computerized decision support system is required to assist management in making effective decisions. This study aims to develop a system for determining the best employees using the Naive Bayes algorithm as a classification method. The Naive Bayes algorithm is a probabilistic classifier that predicts decisions by calculating probability values based on the frequency and combination of attributes within a dataset. The implementation of this method is expected to minimize errors in employee selection caused by limited evaluation criteria and human judgment. The results of this approach indicate that the use of a computerized decision support system can improve the efficiency, accuracy, and objectivity of employee performance assessments compared with conventional methods. Thus, the proposed system can support management in determining the best employees more effectively.