<![CDATA[The Additive Ratio Assessment (ARAS) method is one of the approaches in multi-criteria decision making (MCDM) used to determine the best alternative based on a number of predetermined criteria. The drawback of this method is its heavy reliance on the accuracy of the criterion weighting determination; non-objective weights can lead to biased results. This study aims to improve the accuracy of ranking in multicriteria decision-making through the modification of the ARAS method with a distance-based weighting approach called ARAS-D. The ARAS method, known for its simplicity in calculation, was modified to be more responsive to the distribution of alternative data on each criterion. This distance-based weighting approach objectively determines the weight of the criteria based on variations in data performance, thereby reducing subjectivity in the weighting process. A case study was conducted on the selection of a new store location with six main criteria: rental cost, building area, accessibility, consumer traffic, parking availability, and infrastructure. The results of the evaluation show that the ARAS-D method is able to produce more precise ratings than the standard approach. Store locations with the highest utility value are recommended as the best choice, proving the effectiveness of the method in supporting strategic decisions. The results of the New Store Location 5 alternative rating obtained the highest score with a value of 0.9083, indicating that this location is the most optimal choice overall. This is followed by New Store Location 3 with a value of 0.8617 and New Store Location 1 with a value of 0.8415, which also shows excellent performance against the criteria that have been set. This research contributes to the development of more adaptive and data-based decision-making methods.]]>
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