Comparison of K-Nearest Neighbors (KNN) and Decision Tree with Binary Particle Swarm Optimization (BPSO) in Predicting Employee Performance

Isti Amelia Isnaeni, Sandra Indrian, Muhammad Rizaq Nuriz Zaman, Andi Nugroho

Abstract


Human resource management has a significant role in influencing organizational performance. As a company that seeks to increase objectivity and decision making that relies on data, this research focuses on exploring the application of Machine Learning algorithms as a transformative approach to employee performance evaluation at PT XYZ. When the current process still uses conventional methods such as manual scoring systems and subjective managerial assessments, bias and lack of transparency may occur. This research discusses technical aspects in implementing Machine Learning algorithms using K-Nearest Neighbors (KNN) and Decision Trees . Before carrying out the classification process, the optimization stage is carried out using the Binary Particle Swarm Optimization (BPSO) algorithm to determine the optimal hyperparameter values. The results of the classification process are then evaluated using the Confusion Matrix . The dataset used in this research has 5 classes so it requires a Multi-Class classification ( MCC) approach. This research describes the process of determining the final results of evaluation metrics using the MCC Confusion Matrix with 5 classes. The final results show that the highest F1-Score was obtained from the KNN Algorithm at 84.36% and the Decision Tree Algorithm at 79.8%. Thus, this research contributes to detailing the effectiveness of applying Machine Learning algorithms for evaluating employee performance in the organizational context of PT XYZ

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