Fuzzy Decision Support System for ABC University Student Admission Selection
- DOI
- 10.2991/aebmr.k.220204.024How to use a DOI?
- Keywords
- Corporate Social Responsibility; ROA; Tobin’s Q; Financial Performance; Marketing Cost
- Abstract
New Student Admission (PMB) is a pattern for selecting prospective students. Every year, every University conducts PMB selection, and new student admissions are divided into numerous tracks: the Independent Path. The Basic Competency Exam, TOEFL Prediction, and interviews are commonly used in this selection process. So far, the registrant data has been manually sorted in the selection process. A system that can help determine prospective new students will be required since many student data will be picked for PMB. Because of the system’s assistance, universities can make the selection process more effective. The method used in this study to implement a PMB decision system is Fuzzy Mamdani. The method in this study has a sequence of stages: first doing fuzzification, second using inference evaluation rules that have been determined, and finally, defuzzification to get the final results of the calculations. This system was tested by comparing the system’s results to the actual results obtained according to the design. The system testing results received a 96 percent approval rating. As a result, the University’s new student admissions system can be used to help streamline the formerly manual selection process.
- Copyright
- © 2022 The Authors. Published by Atlantis Press International B.V.
- Open Access
- This is an open access article under the CC BY-NC license.
Cite this article
TY - CONF AU - Muchtar Ali Setyo Yudono AU - Riyan Mirdan Faris AU - Aryo De Wibowo AU - Muhammad Sidik AU - Falentino Sembiring AU - Sankan Fahmi Aji PY - 2022 DA - 2022/02/10 TI - Fuzzy Decision Support System for ABC University Student Admission Selection BT - Proceedings of the International Conference on Economics, Management and Accounting (ICEMAC 2021) PB - Atlantis Press SP - 230 EP - 237 SN - 2352-5428 UR - https://doi.org/10.2991/aebmr.k.220204.024 DO - 10.2991/aebmr.k.220204.024 ID - Yudono2022 ER -