Sensitivity Analysis of a Simple Additive Weighting-Based Decision Support System for Strategic E-Learning Evaluation

Abstract

The implementation of e-learning often faces obstacles in the objectivity of student performanceevaluations which have an impact on the inaccuracy of managerial decision-making. This studyaims to design a Decision Support System (SPK) using the Simple Additive Weighting (SAW)method which is integrated with sensitivity analysis. Four main criteria were used: forum activity(25%), task accuracy (30%), evaluation value (30%), and duration of access (15%). Thesimulation results showed that the system succeeded in classifying students into three categoriesof adaptivity, with Citra (A3) being the highest ranked (score of 0.98). Crucial findings from the  sensitivity analysis showed that although the weight of the interaction was increased by 40%, theranking order remained robust, but the score in the passive student group decreased significantlyto 0.46 (below the critical threshold of 0.50). This proves that the model not only functions as aranking tool, but also as a precise early warning system for university management to carry outdata-driven preventive interventions.