METHOD COMPARISON IN THE DECISION SUPPORT SYSTEM OF A SCHOLARSHIP SELECTION

Abstract

Commonly, the current scholarship selection process has different targets and various criteria for its prospective scholarship recipients. This causes the decision-making process for scholarship selection to be complex, whereas in the general scholarship selection is time-limited. The solution that can be done is to use a DSS (Decision Support System) to improve consistency and speed up decision-making. The available methods for making a DSS used in this study are the Analytical Hierarchy Process, TOPSIS, and the second model using a deep learning approach. The performance of the DSS will then be evaluated using a Confusion Matrix to determine the cost level of each DSS and analyze the strengths and weaknesses of each DSS. The DSS model with the AHP-TOPSIS approach has been successfully created, with the accuracy performance for introducing data on merit, bidikmisi, and independent scholarship schemes are 56.72%, 65.21%, and 95.87%, respectively. While the DSS model with a deep learning approach has been successfully created with accuracy performance of 71.93%, 100%, and 100%, respectively. There are considerable differences between these two approaches. This may be due to the weighting process in the AHP approach which cannot be carried out with precision.