| dc.description.abstract | This paper examines the utilization of fuzzy logic in human resource management (HRM) to tackle the complex
nature of performance assessment in organizations. Conventional performance appraisal techniques frequently
neglect the uncertainties and ambiguities associated with evaluating qualitative metrics like leadership,
teamwork, and communication. Conversely, fuzzy logic provides a methodical framework for addressing these
ambiguities, so improving the equity, precision, and thoroughness of assessments. The study examines the
application of fuzzy inference algorithms to evaluate six fundamental performance metrics: job knowledge, work
quality, productivity, leadership, teamwork, and communication. The study uses verbal and numerical quantifiers
to formulate probabilistic principles and categorizes performance results into five classifications: never, seldom,
sometimes, often, and always. The dual-variable methodology facilitates comprehensive assessments at the
individual, team, and organizational tiers. Research indicates that fuzzy logic markedly improves decision-making
in performance management by incorporating expert information and mitigating subjective uncertainties. The
fuzzy modelling outcomes correspond with real-world applications in the university setting, providing a solid
foundation for measuring qualitative advantages such as employee engagement, organizational efficiency, and
value. This study utilizes fuzzy techniques, such as Mamdani and Tsukamoto systems, to offer organizations
practical insights for optimizing their appraisal systems in accordance with evolving HR requirements.
Subsequent investigations may broaden these methodologies to more sectors, evaluating their enduring influence
on organizational achievement. This study highlights the transformative capacity of fuzzy logic as a mechanism
for fair and efficient performance assessments in intricate settings. | en_US |
