ANALYSIS OF STUDENT ACADEMIC ACHIEVEMENT LEVELS USING FUZZY LOGIC
Original Full-Text Article
Download published version for reading and archivingAbstract
The purpose of this study is to use fuzzy logic to analyze and retrieve data about student performance levels. This data will later be used to facilitate the analysis of the performance of SMP Negeri 5 Batusangkar students. Data for this study were obtained through observation and literature review from a variety of sources. The results of this survey analysis make it easier for teachers to analyze student performance without manual searches, and the ability to quickly process values using the provided media such as Microsoft Excel, Access, etc. We provide accurate and accurate data. When judging the characteristics of a student's level of academic performance, she complies with the Order of the Minister of Education, Culture, Research and Technology (Permendikbudristok) No. 21 of 2022 on Educational Evaluation Standards. Analyzing student performance levels is performed using a fuzzy logic database by mapping student results based on variables. Ethics of use, knowledge and aspects of information (attitudes). This technique is tested using Microsoft Excel to query the designed data.
Keywords:
Author Biographies
Faculty of Ushuluddin Adab and Dakwah, Universitas Islam Negeri Mahmud Yunus Batusangkar, Tanah Datar Regency, Sumatera Barat Province, Indonesia
How to Cite
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License .
- Share: You are free to copy, distribute, and transmit the work in any medium or format.
- Adapt: You are free to remix, transform, and build upon the work for any purpose, even commercially.
- Attribution: You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
References
Total: 17 References- Riyanto, A.D., Marcos, H., Karini, Z. and Amin, K.M., 2017, November. Fuzzy logic implementation to optimize multiple inventories on micro small medium enterprises using mamdani method (Case Study: Pekanita, Kroya, Cilacap). In 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE) (pp. 261-266). IEEE. DOI: 10.1109/ICITISEE.2017.8285508.
- Samsinar, R. and Susanto, H., 2018. RANCANG BANGUN PENGATURAN TEMPERATUR UDARA PADA KONVEYOR INDUSTRI ELEKTRONIK MENGGUNAKAN KENDALI LOGIKA FUZZY. Prosiding Semnastek.
- Wang, C., 2015. A study of membership functions on mamdani-type fuzzy inference system for industrial decision-making. Lehigh University.
- Martin, H., 2019. Measuring Qualitative Performance Criteria with Fuzzy Sets. In Business Information Systems Workshops: BIS 2019 International Workshops, Seville, Spain, June 26–28, 2019, Revised Papers 22 (pp. 417-423). Springer International Publishing. DOI: 10.1007/978-3-030-36691-9_35.
- Papadimitriou, S., Chrysafiadi, K. and Virvou, M., 2019. FuzzEG: Fuzzy logic for adaptive scenarios in an educational adventure game. Multimedia Tools and Applications, 78, pp.32023-32053. DOI: https://doi.org/10.1007/s11042-019-07955-w.
Similar Articles
Articles sharing related keywords and machine learning classifications: