Classification of Employee Performance Based on Evaluation Data Using the K-Nearest Neighbor (K-NN) Method: (A Case Study of PTPN IV Regional II Tanjung Jati Estate)

Abstract

Employee performance evaluation is an important aspect of supporting organizational decision-making to improve productivity and work effectiveness. At PTPN IV Regional II, the performance evaluation process for fixed-term contract employees PKWT is still carried out manually based on attendance, production, and achievement data, making the process relatively time-consuming and increasing the possibility of errors in determining employee performance categories. Therefore, this study aims to apply the K-Nearest Neighbor (K-NN) algorithm to classify employee performance by utilizing RapidMiner as a tool for data processing and model evaluation. Before the classification process, the data were normalized using the Min-Max Normalization method to ensure that all attributes had the same value range. The training and testing processes were then performed using the K-Nearest Neighbor algorithm based on Euclidean Distance calculations. This study employed attendance, production, and achievement as the classification attributes to categorize employee performance into Excellent, Good, Fair, and Poor. The experimental results showed that the proposed classification model achieved an accuracy of 90.32%, indicating that the K-Nearest Neighbor algorithm can provide reliable classification results and effectively support employee performance evaluation in a faster, more objective, consistent, and accurate manner.