Employee performance evaluation is a critical issue in organizational management, as identifying the underlying factors influencing employee satisfaction and productivity enables managers to make more informed decisions. This study aims to discover hidden patterns in employee performance through the integration of Rough Set Theory and data mining techniques, using a case study approach in a manufacturing company. Rough Set Theory is applied to reduce redundant attributes and extract decision rules from large employee datasets, while data mining algorithms are employed to uncover latent relationships between job satisfaction, loyalty, and performance levels. The empirical analysis, based on data collected from a structured questionnaire and organizational performance records, reveals a set of meaningful patterns describing how various personal and organizational factors jointly impact employee effectiveness. The findings illustrate that the proposed hybrid approach provides a transparent and interpretable framework for performance analysis, supporting strategic human resource decisions and continuous improvement in employee management.

Rough Set Theory Data Mining Employee Performance Hidden Patterns Human Resource Management