<![CDATA[Identifying high-performing employees is a critical component of human resource management, as it directly influences organizational productivity, work climate, service quality, and strategic goal achievement. However, conventional employee performance assessments often rely on subjective managerial judgment, making them vulnerable to personal bias and inconsistencies that can lead to dissatisfaction, decreased morale, and internal conflict. To address these challenges, Decision Support Systems (DSS) that employ data-processing algorithms have been increasingly adopted to enhance objectivity and accuracy in employee evaluation. This study conducts a Systematic Literature Review (SLR) of 25 scholarly publications published between 2017 and 2025 and indexed in nationally and internationally recognized databases. The analysis focuses on the types of algorithms applied, system development methodologies, and their relevance to optimizing the identification of top-performing employees. The findings indicate that multi-criteria decision-making methods, particularly the Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW), are the most frequently used algorithms, followed by TOPSIS, PROMETHEE, MABAC, ELECTRE, Weighted Product, SMART, and hybrid approaches. In terms of system development, several studies did not explicitly specify their methodology, while others adopted structured approaches such as the System Development Life Cycle (SDLC) and Waterfall models. This review highlights methodological trends, identifies research gaps, and proposes potential directions for future studies on algorithm-based DSS applications in employee performance evaluation]]>
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