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Decision Support System (DSS) is a system used to assist decision makers in determining the best alternative based on certain criteria. Determining retirement eligibility for ASN (State Civil Apparatus) employees is one of the important aspects in human resource management, where this decision affects the future of employee careers, their welfare, and state budget management. This study proposes the development of a decision support system for determining retirement eligibility for ASN employees using the Multi-Attribute Decision Making (MADM) method. The MADM method is a decision-making technique that considers several criteria and alternatives to produce optimal decisions. In this study, the criteria used to determine retirement eligibility include retirement age, length of service, work performance, health, and other factors relevant to the applicable pension policy. The method used in this system includes several steps, namely identifying criteria, assigning weights to each criterion, and evaluating alternatives based on these criteria using calculation techniques such as AHP (Analytical Hierarchy Process) or TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). The results of this evaluation will provide recommendations on whether an employee is eligible for retirement or can still continue his career. The developed system is expected to facilitate the decision-making process regarding ASN employee retirement by providing objective and transparent analysis, so that the decisions taken are more appropriate and in accordance with applicable policies. Thus, this system can improve efficiency and effectiveness in human resource management in government agencies.

This research examines the application of the Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods in determining the best graduate students in the Ners Professional Study Program at Muhammadiyah University of East Kalimantan. The third step of the AHP method involves converting the values in the pairwise comparison matrix to decimal form, which is then normalized to calculate the priority weight of each criterion and sub-criteria. Next, checking the logic of the criteria and designing AHP-TOPSIS for ranking were carried out. The analysis showed that AHP resulted in 5 ranking changes with a percentage change of 4.4%, while TOPSIS resulted in 3 ranking changes with a percentage change of 3.9%. From these results, the AHP-TOPSIS method proved to have an accuracy of 83.00%. This article also presents a comparison between AHP, TOPSIS, and AHP-TOPSIS methods, where the best student selected is Dinda Ayu Framaisella. This research provides practical guidance for decision makers in solving multi-criteria problems and contributes to the selection of the best graduate students with a comprehensive and accurate approach.