<![CDATA[Employee performance evaluation is a critical aspect of human resource management, directly influencing productivity, promotion decisions, and career development planning. Traditional approaches often fail to capture hidden patterns in complex employee data, making machine learning (ML) a promising solution for more accurate and objective classification. This study aims to model and compare two ML algorithms, K-Nearest Neighbor (KNN) and Random Forest, for employee performance classification, followed by scenario-based simulation using the best-performing model. The research employed a quantitative computational approach with a dataset of 100,000 employee records and 20 features. Preprocessing steps included feature selection, binarization of performance scores, handling class imbalance using Synthetic Minority Over-sampling Technique (SMOTE), and feature engineering to enrich data representation. The dataset was split into 80% training and 20% testing. KNN was first built as a baseline model, then optimized through hyperparameter tuning using GridSearchCV with cross-validation. Random Forest was implemented with 100 decision trees and bootstrap sampling to enhance accuracy and stability. Results show that the tuned KNN model achieved an accuracy of 70.88%, improving from 63.85% baseline. However, Random Forest outperformed KNN significantly, reaching 97.17% accuracy with lower error rates. Scenario simulations using Random Forest demonstrated practical applicability in predicting employee performance based on work behavior profiles. In conclusion, Random Forest provides a more robust and reliable model for employee performance classification compared to KNN. The study contributes by integrating algorithm comparison with simulation design, offering actionable insights for human resource decision-making. Future research may explore additional ensemble methods and real-time evaluation frameworks.]]>
