DOI: https://doi.org/10.14710/jmasif.17.1.78414

Comparison of Objective Weighting Methods in SAW and Their Effect on Alternative Ranking Results

1Department of Digital Business, Zhejiang Technical Institute of Economics, China

2Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia, Indonesia

3Faculty of Engineering and Computer Science, Universitas Bina Sarana Informatika, Indonesia

Received: 10 Oct 2025; Revised: 28 Jan 2026; Accepted: 18 Feb 2026; Published: 20 Feb 2026.
Open Access Copyright (c) 2026 The authors. Published by Department of Informatics Universitas, Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

Determining the weights of criteria is a vital stage in multi-criteria decision making, yet it often suffers from evaluator subjectivity and unstable results when relying on expert judgment. Dependence on human perception may also lead to inconsistencies among criteria, highlighting the need for objective, data-driven approaches to generate rational and measurable weights. This study analyzes and compares six objective weighting methods—Entropy, MEREC, RECA, G2M, LOPCOW, and CRITIC—in the selection of new store locations. Each method applies distinct mathematical principles but shares a common foundation in objective data analysis, free from subjective bias. The findings reveal that criterion S5 consistently receives the highest weight, emphasizing its dominant role in decision outcomes. Using the Simple Additive Weighting (SAW) method, New Store Location 5 ranks first across all weighting techniques, followed by Locations 3 and 8. The Spearman correlation test confirms a high level of consistency among methods, with coefficients of 1 for RECA, G2M, and LOPCOW, and 0.9879 for Entropy, MEREC, and CRITIC. These results demonstrate that objective weighting methods produce stable and reliable evaluations, effectively supporting data-based strategic decision making in multi-criteria contexts.

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Section: Research Article
Language : EN
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