METHOD COMPARISON IN THE DECISION SUPPORT SYSTEM OF A SCHOLARSHIP SELECTION
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...
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Published in | Jurnal Ilmiah Kursor: Menuju Solusi Teknologi Informasi Vol. 11; no. 2; p. 75 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Informatics Department, Engineering Faculty
11.01.2022
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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ISSN: | 0216-0544 2301-6914 |
DOI: | 10.21107/kursor.v11i2.263 |