Optimization of Simple Additive Weighting Method in Assessment of Research Reviewer Selection

Abstract

Quality research will not be separated from controlling systems that require a review mechanism. This demand considers it necessary to form an assessment committee or reviewer that ensures that all processes proceed towards the target target. The internal reviewer selection process is carried out by looking at several requirements of each prospective reviewer. The selection process is carried out by looking at the requirements files one by one. For this reason, it is necessary to optimize the method that is able to manage the assessment data of prospective reviewers who have the highest rating value from the results of weight calculations. Decision making in determining internal reviewers requires a method that can provide optimal decision results in terms of relatively fast processing time. The decision support method applied in determining internal reviewers is Simple Additive Weighting (SAW). The reason for choosing the SAW method in this study, the method has a basic concept that is used to find weight values on the performance rating of each alternative on all attributes. The SAW method is commonly known as the weighted summation method. There are six criteria used and fifty-five records for alternatives used. The results of the SAW method ranking obtained by A20 have the highest preference value of 0.77. This study shows the optimality of the SAW method in providing decision results based on an accuracy test value of 80%.