Comparison between Multiple Attribute Decision Making Methods through Objective Weighting Method in Determining Best Employee

Andre Hasudungan Lubis

doi:10.55524/ijircst.2023.11.2.10

Abstract

Multiple Attribute Decision Making (MADM) is a popular method to be selected in numerous studies in solving decision-making cases. Methods like SAW, WASPAS, SMART, and WP are preferred among researchers to be used for many purposes. However, the best method still not compared in determining best employee. Hence, the study conducted the comparison between the methods by using the Rank Similarity Index (RSI). The index is used to express the most appropriate method. In terms of weighting, we propose the D-CRITIC method as the tool to support the comparison procedure. Moreover, we select the bus driver as the sample case of the study with total of 10 candidates are nominated to be chosen as the best. The company has given the rank list before, so we just compare the actual rank with the result of MADM methods calculations. The result shows that SAW and WASPAS are the methods with the highest similarity towards the rank. Furthermore, these methods also reach the great score of the RSI between the others.

Keywords

MADM, Comparison, Best Employee, Objective Weighting, RSI.

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Cites this article as

[Andre Hasudungan Lubis, Nurul Khairina, Muhammad Fikri Riandra (2023), Comparison between Multiple Attribute Decision Making Methods through Objective Weighting Method in Determining Best Employee, International Journal of Innovative Research in Computer Science & Technology (IJIRCST), Vol-11, Issue-2, Page No-50-57], (ISSN 2347 - 5552). www.ijircst.org

Corresponding Author

Andre Hasudungan Lubis

Lecturer, Informatic Engineering, Faculty of Engineering, Universitas Medan Area, Medan, Indonesia

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