<![CDATA[In the era of digital transformation, organizations face challenges in evaluating employee performance objectively and based on data. Traditional performance appraisal systems often contain subjectivity and limitations in data integration, making them less effective in dynamic work environments. This study aims to develop a performance evaluation model based on digital footprints using machine learning and multivariate analysis. Digital footprints include work activity data (daily working hours, screen time, meetings, and emails), wearable data (physical steps, sleep duration, and stress levels), satisfaction (work-life balance, organizational support), capability (tech skills score, job level, and training), and organizational data (salary, incentives, and overtime). Principal Component Analysis (PCA) is used to reduce data dimensions and identify key performance indicators. Three machine learning algorithms—Decision Tree, Random Forest, and Gradient Boosting—are applied to classify employee performance into Low, Average, Good, and Excellent categories. Model evaluation is performed using accuracy, precision, recall, and F1-score metrics. The results show that the Gradient Boosting model combined with PCA delivers the best performance with an accuracy of 0.887 and an F1-score of 0.884. The application of PCA significantly improved classification model performance by reducing noise and multicollinearity in high-dimensional data. These findings highlight the great potential of leveraging employees' digital behavioral data to build a transparent and adaptive performance evaluation system. This study contributes to the development of intelligent HR management and supports data-driven decision-making in modern organizations.]]>
