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Venus - Venus Jurnal Publikasi Rumpun Ilmu Teknik  - Vol. 3 Issue. 1 (2025)

Sistem Pendukung Keputusan Penentuan Kelayakan Pensiunan Pegawai ASN dengan Model MADM

Arnoldus Lamber Gai, Cecilia D.P.B Gabriel, Agustina Purnami Setiawi,



Abstract

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.







DOI :


Sitasi :

0

PISSN :

3031-5026

EISSN :

3031-3481

Date.Create Crossref:

12-Apr-2025

Date.Issue :

09-Jan-2025

Date.Publish :

09-Jan-2025

Date.PublishOnline :

09-Jan-2025



PDF File :

Resource :

Open

License :

https://creativecommons.org/licenses/by-sa/4.0