Abstract:
Decision support systems generally apply criteria weighting as an important stage in
the resolution process. The method that applies weighting criteria is Multi-Attributive
Border Approximation Area Comparison (MABAC). However, the weighting of criteria
is mostly done subjectively by the decision maker or team involved. In the case study of
selecting outstanding students, there are 10 variations in the weight of the criteria. This
makes it difficult to determine the best criteria weight values. Therefore, an evaluation
was carried out on the MABAC ranking results based on 10 variations of criteria
weights using distance-based methods, namely Manhattan Distance and Euclidean
Distance. The method is used to measure the distance between the MABAC ranking and
the actual ranking. If the resulting distance is small then the ideal solution can be
identified in determining the best weight. The ranking evaluation results of the two
methods show that weight 6 has the smallest distance. This means that weight 6 is the
best weight because it produces a ranking that is close to the actual ranking
