SENSITIVITY OF OBTAINING ERRORS IN THE COMBINATION OF FUZZY AND NEURAL NETWORKS FOR CONDUCTING STUDENT ASSESSMENT ON E-LEARNING
DOI:
https://doi.org/10.53695/injects.v2i1.412Keywords:
Covid-19, fuzzy neural network clone Logic, Class Online, the internetAbstract
Utilization of information technology in the midst of the covid-19 pandemic, including in the field of education, namely by holding On-line Classes. The development of information technology is also growing rapidly, especially in the field of education. Distance learning facilities via the Internet allows maha students undertake independent learning so as to facilitate the learning process. In addition, with the development of the system, it is hoped that this facility will be able to direct the ongoing learning process, so that the learning process that occurs resembles the learning process in the classroom. In conducting an assessment, a lecturer/teacher must pay attention to the rules of assessment. However, because there are no clear rules in conducting the assessment, the assessment process must be designed adaptively to adjust its calculations to the assessment rules of the lecturers / instructors who use the system. In this study, the fuzzy logic method approach and the artificial neural network method were used in calculating the assessment of learning outcomes.References
Najjaran, H., Sadiq, R., Rajani, B.B., 2006, Sistem pakar fuzzy untuk menilai korosi cast/ductile iron pipes from backfill properties cor / pipa besi ulet dari properti backfill, Computer-Aided Teknik Sipil dan Infrastruktur, v. 21, no. v. 21, no.1, January 2006, pp. 67-77.1, Januari 2006, hlm 67-77
IP Sari, H Mawengkang, S Efendi., 2019, Fuzzy Analytical Hierarchy Process (FAHP) Ekspansi Untuk Inovasi Kerangka Pengukuran Kinerja,
Infotekjar : Jurnal nasional Informatika dan Teknologi Jaringan- Vol. 3 No. 2, Maret 2019.
Lahsasna, A., Ainon, N., Wah, T., 2010, Credit Scoring Model Menggunakan Perangkat Lunak Computing Methods: A Survey Metode Komputasi: Sebuah Survei, Journal Arab Internasional Teknologi Informasi, Vol.7, No. 2, April 2010.
Vasconcelos, G.C., Adeodato, L., Monteiro, P., 2000, A Neural Network Based Solution for the Credit Risk Assessment Problems, Proceedings of the IV Brazilian Conference on JST, pp. 269-274, Brazil.
George J. Klir, Bo Yuan, 1995, Fuzzy Sets and Fuzzy Logic (Theory and Applications), Prentice-Hall Inc.
Negnevitsky, M., 2002, Artificial Intelligence : a guide to intelligent systems, Pearson Education, Inc., England.
Alonso, J.A., 2006. Consistency in The Analytic Hierarchy Pocess (AHP) : A New Approach. International Journal of Uncertainly,Fuzziness ang Knowledge-Based Systems 14 (4), 445-459.
Rauter,M., 1995. About a Framework for Information and Information Processing of Learning System, Prooceedings of the IFIP International Working Conference on Information System Concepts, 54-69.
T. L. Saaty, The Analytic Hierarchy Process,McGraw Hill International Book, New York, NY,USA, 1980.
T. L. Saaty, “How to make a decision: The analytic hierarchy process,” Interfaces, vol. 24, pp. 19–43, 1994
