Prediksi Penempatan Pegawai Menggunakan Algoritma Supervised Machine Learning dan Rules Based Experts: Studi Kasus di Institusi Pendidikan - Romdhoni, Bayu Suciono; Denny (The Indonesian Journal of Computer Science, 2024)
Education plays a crucial role in shaping the future of a nation. To maintain the quality of education, effective human resource management is essential in educational institutions. This study addresses the challenges of employee’s placement under the Educational Institution. According data from December 2021 to May 2024, only 2,452 out of 41,722 employees were reassignment, which is significantly below the target set by regulation. This study evaluates several supervised machine learning algorithms, including Gaussian Naive Bayes, Decision Tree, Support Vector Machine, and Random Forest. Random Forest emerges as the most suitable algorithm due to its superior accuracy, precision, recall, and F1 Score. Following the evaluation of the chosen algorithm, the deployment phase includes comprehensive data preprocessing steps, such as handling missing values, data normalization, and categorical feature encoding. This system integrates with Google API for geospatial data, ensuring accurate and efficient decision-making.