Sensitivity Analysis of The AHP and TOPSIS Methods for The Selection of The Best Lecturer Base on The Academic Achievement

Abstract

In order to resolve cases related to an alternative selection problems (MADM problems), various methods have been applied. Many methods are used to solve the MADM problems, which sometimes give different results. To resolve MADM problem, we can use AHP and TOPSIS methods. The advantages of AHP method are it can provide solutions through the analysis of quantitative and qualitative decision. In addition, it presented simple solution using hierarchical model. On the other hand, TOPSIS method gives a simple concept and is easy to implement, computationally efficient, and easy to be understood. In this research, we aim to comparing the AHP and TOPSIS methods to solve problem best lecturer selection base on academic achievement. The best lecturer selection is analyses with Chung-Hsing algorithm and using AHP and TOPSIS method. From the experiment result, it can be seen that many changes occur to alternative grade base on those methods. The grade changes show which methods that the most appropriate to be used. Then, based on the chosen method we can determine the best alternative. The research result shows that TOPSIS has many changes rather than AHP. TOPSIS results have variance from 17.39% to 72.05%. On the other hand AHP have showed 0% to 44.72% for experiment weight among 1.1 to 2. We conclude that TOPSIS method is more appropriate than AHP method so it can be used for select the best lecture base on the academic achievement.