Analisis Kinerja Smarter untuk Pembobotan Atribut pada Mabac dalam Pengambilan Keputusan

Abstract

MABAC is a decision support method developed by Pamucar and Cirovic to solve decision-making problems involving multiple attributes. In several previous studies, the weighting of attributes on the MABAC was carried out in a subjective way. Attribute weighting in MABAC in this way is considered less effective because there is no validation to produce weight values, so that the accuracy of the final ranking results in the process of determining the best alternative is less accurate. Therefore, it is necessary to apply a weighting method to the MABAC in order to increase its accuracy. In this study, the authors propose SMARTER as attribute weighting considering that SMARTER is weighting objectively. To find out the results of the SMARTER performance analysis for attribute weighting in MABAC, MABAC testing with subjective weighting and MABAC with weights with SMARTER weighting is then calculated using the Relative Standard Deviation (RSD) difference in accuracy. After the testing process, the accuracy of the MABAC with subjective weighting 11% is obtained and the accuracy of the MABAC with the weighting of SMARTER 24%. From the accuracy results obtained, it is known that the MABAC with SMATER weighting has increased accuracy by 24%.