Modification of Multi-Attributive Border Approximation Area Comparison (MABAC) to Improve Multi-Criteria Assessment

Hariyanto Hariyanto; Ade Christian; M. Sinta Nurhayati; Bibit Sudarsono

doi:10.33365/jatika.v6i1.15

Hariyanto Hariyanto Universitas Bina Sarana Informatika
Ade Christian Universitas Bina Sarana Informatika
M. Sinta Nurhayati Universitas Bina Sarana Informatika
Bibit Sudarsono Universitas Bina Sarana Informatika

DOI: https://doi.org/10.33365/jatika.v6i1.15

Keywords: Accuracy, MABAC, MCDM, Modification, Objectivity

Abstract

Multi-criteria decision making (MCDM) is a field of study in decision-making that focuses on selecting or ranking alternatives based on several competing criteria. Multi-attributive border approximation area comparison (MABAC) is one of the methods in MCDM that is designed to evaluate and select the best alternative based on relevant criteria. The weakness of the MABAC method in the aspect of criterion weighting mainly lies in its dependence on the external weighting method. The data used in the Best Staff Selection case study included staff performance assessments based on several key criteria. The results of this data are then used in MCDM to determine the best staff based on the weight of objectively established criteria. The purpose of this study is to modify the MABAC method by integrating the geometric average method which aims to improve accuracy and objectivity in multi-criteria assessment. The results of the ranking with the MABAC-G method for the selection of the best employees show that employee 5 obtained the highest score of 0.2868 so that it is the best alternative in this assessment. The results of the comparison of the ranking of alternative selection of the best employees using the ranking from the company and the MABAC-G method obtained a Pearson correlation value of 0.9511 which shows that there is a very strong relationship between the two assessment systems. The application of research findings from MABAC-G in the future can be used in various fields that require multi-criteria decision-making with complex and uncertain data.

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