<![CDATA[One of the requirements for any company's success is the efficient utilization of human resources in its operations. There are several different tasks that fall within the Human Resources Management (HRM) role. This study uses the GDSS technique, combining TOPSIS for the second decision maker and AHP-SAW for the first decision maker. There are two decision makers (DMs), as is the case. After that, each decision maker's ranking will be processed using the BORDA technique. This study concludes that there are differences in the outcomes from DM1 and DM2. This is a result of the propensity for every decision-maker to offer a unique assessment. While DM2 employs the scoring technique using percentages to determine sub-parameter weighting and the TOPSIS method for ranking, DM1 uses the AHP method for sub-parameter weighting and SAW for ranking. However, the researchers integrated the two decision makers with a group decision support system utilizing the BORDA approach, accounting for the decision makers' respective levels of interest. The findings highlight the significant contribution of this research in demonstrating how the integration of these methods effectively addresses differences in evaluation approaches among decision-makers, particularly in the context of software engineering, ensuring a transparent and data-driven selection process. The GDSS model was tested on a case study of software engineer selection, demonstrating a high level of accuracy in identifying the most suitable candidates for organizational needs. Furthermore, the results of the GDSS aligned with the decisions made by the case study company, validating the effectiveness of the proposed model. The practical application of this model can be adopted by companies to enhance the efficiency and quality of recruitment processes.]]>
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