DOI: https://doi.org/10.14710/jfma.v7i2.21170

Pythagorean Fuzzy Set and Its Application to Determining Student Concentration Using Max-Min-Max Composition

Imam Hafiidz Nuur  -  Department of Mathematics, Sunan Kalijaga State Islamic University Yogyakarta, , Indonesia
*Arif Munandar  -  Department of Mathematics, Sunan Kalijaga State Islamic University Yogyakarta, , Indonesia

Citation Format:
Abstract
Pythagorean fuzzy set is a generalization of intuitionistic fuzzy set. As intuitionistic fuzzy set that can be used to help in solving problems regarding decision making, pythagorean fuzzy set can also be done for the same thing.  In the pythagorean fuzzy set, a max-min-max composition relation will be formed and used it to solve decision-making problems. Through this research, decision making in determining the concentration for students of the Mathematics undergraduates program at Sunan Kalijaga State Islamic University Yogyakarta is discussed based on data on student grades in compulsory courses that have been taken by students until the 4th semester. Concentration that is in line with the interests and abilities is expected to facilitate the writing of the student's final project.
Fulltext

Article Metrics:

Article Info
Section: FUNDAMENTAL MATHEMATICS AND APPLICATIONS
Language : EN
Recent articles
MULTINOMIAL LOGISTIC REGRESSION TO DETERMINE FACTORS INFLUENCING THE SELECTION OF HEALTH CARE FACILITIES IN INDONESIA Pythagorean Fuzzy Set and Its Application to Determining Student Concentration Using Max-Min-Max Composition A New Path to Accurate Risk Adjustment: Applying CreVaR for Better Financial Reporting under IFRS 17 More recent articles
  1. L. A. Zadeh, “Fuzzy Set,” Inf. Control, vol. 8, pp. 338–353, 1965
  2. L. Tyagi and S. Singal, “Application of Fuzzy Logic Control Systems in Military Platforms,” in 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), IEEE, Jan. 2019, pp. 397–402. doi: 10.1109/CONFLUENCE.2019.8776955
  3. C. G. Amza and D. T. Cicic, “Industrial Image Processing Using Fuzzy-logic,” Procedia Eng., vol. 100, pp. 492–498, 2015, doi: 10.1016/j.proeng.2015.01.404
  4. N. A. Korenevskiy, “Application of Fuzzy Logic for Decision-Making in Medical Expert Systems,” Biomed. Eng. (NY)., vol. 49, no. 1, pp. 46–49, May 2015, doi: 10.1007/s10527-015-9494-x
  5. A. K. Varshney and V. Torra, “Literature Review of the Recent Trends and Applications in Various Fuzzy Rule-Based Systems,” Int. J. Fuzzy Syst., vol. 25, no. 6, pp. 2163–2186, 2023, doi: 10.1007/s40815-023-01534-w
  6. M. Demetgul, “Washing Machine Using Fuzzy Logic,” Autom. Control Intell. Syst., vol. 2, no. 3, p. 27, 2014, doi: 10.11648/j.acis.20140203.11
  7. J. M. Belman-Flores, D. A. Rodríguez-Valderrama, S. Ledesma, J. J. García-Pabón, D. Hernández, and D. M. Pardo-Cely, “A Review on Applications of Fuzzy Logic Control for Refrigeration Systems,” Appl. Sci., vol. 12, no. 3, 2022, doi: 10.3390/app12031302
  8. M. L. Prakasta and A. Munandar, “Pewarnaan Fraksional Fuzzy pada Graf Fuzzy Beserta Aplikasinya dalam Penjadwalan Ujian,” J. Fourier, vol. 12, no. 1, pp. 1–9, Apr. 2023, doi: 10.14421/fourier.2023.121.1-9
  9. I. N. Ramadhani and A. Munandar, “Sturuktur Graf Fuzzy dan Aplikasinya pada Pengambilan Keputusan dalam Identifikasi Layanan Perjalanan,” J. Fourier, vol. 13, no. 1, pp. 20–29, Apr. 2024, doi: 10.14421/fourier.2024.131.20-29
  10. K. T. Atanassov, “Intuitionistic fuzzy sets,” Fuzzy Sets Syst., vol. 20, no. 1, pp. 87–96, 1986, doi: 10.1016/S0165-0114(86)80034-3
  11. K. Atanassov and G. Gargov, “Interval valued intuitionistic fuzzy sets,” Fuzzy Sets Syst., vol. 31, no. 3, pp. 343–349, Jul. 1989, doi: 10.1016/0165-0114(89)90205-4
  12. P. Melo-Pinto, P. Couto, H. Bustince, E. Barrenechea, M. Pagola, and J. Fernandez, “Image segmentation using Atanassov’s intuitionistic fuzzy sets,” Expert Syst. Appl., vol. 40, no. 1, pp. 15–26, Jan. 2013, doi: 10.1016/j.eswa.2012.05.055
  13. Y. Anggara and A. Munandar, “Implementation of Hybrid RNN-ANFIS on Forecasting Jakarta Islamic Index,” Jambura J. Math., vol. 5, no. 2, pp. 419–430, Aug. 2023, doi: 10.34312/jjom.v5i2.20407
  14. S. Gumus, M. Kucukvar, and O. Tatari, “Intuitionistic fuzzy multi-criteria decision making framework based on life cycle environmental, economic and social impacts: The case of U.S. wind energy,” Sustain. Prod. Consum., vol. 8, pp. 78–92, Oct. 2016, doi: 10.1016/j.spc.2016.06.006
  15. J. Bajaj and S. Kumar, “A new intuitionistic fuzzy correlation coefficient approach with applications in multi-criteria decision-making,” Decis. Anal. J., vol. 9, p. 100340, Dec. 2023, doi: 10.1016/j.dajour.2023.100340
  16. X. Yuan, H. Li, and E. S. Lee, “On the definition of the intuitionistic fuzzy subgroups,” Comput. Math. with Appl., vol. 59, no. 9, pp. 3117–3129, May 2010, doi: 10.1016/j.camwa.2010.02.033
  17. M. Gulzar, M. H. Mateen, D. Alghazzawi, and N. Kausar, “A Novel Applications of Complex Intuitionistic Fuzzy Sets in Group Theory,” IEEE Access, vol. 8, pp. 196075–196085, 2020, doi: 10.1109/ACCESS.2020.3034626
  18. T. Chaira, Fuzzy Set and Its Extension. 2019. doi: 10.1002/9781119544203
  19. K. T. Atanassov, “Intuitionistic Fuzzy Sets. In: Intuitionistic Fuzzy sets. Studies in Fuzziness and Soft Computing.,” Heidelberg: Springer;, vol. 35. pp. 1–137, 1999
  20. R. R. Yager, “Pythagorean Membership Grades in Multicriteria Decision Making,” IEEE Trans. Fuzzy Syst., vol. 22, no. 4, pp. 958–965, Aug. 2014, doi: 10.1109/TFUZZ.2013.2278989
  21. R. R. Yager, “Pythagorean fuzzy subsets,” in 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), IEEE, Jun. 2013, pp. 57–61. doi: 10.1109/IFSA-NAFIPS.2013.6608375
  22. M. Kirişci and N. Şimşek, “Decision making method related to Pythagorean Fuzzy Soft Sets with infectious diseases application,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 8, pp. 5968–5978, Sep. 2022, doi: 10.1016/j.jksuci.2021.08.010
  23. B. Sarkar, D. Chakraborty, and A. Biswas, “Development of type-2 Pythagorean fuzzy set with its application to sustainable transport system selection,” Appl. Soft Comput., vol. 142, p. 110332, Jul. 2023, doi: 10.1016/j.asoc.2023.110332
  24. J. Wu, G. Zhao, N. Wang, Y. Xu, and M. Wang, “Research on optimization of mining methods for broken ore bodies based on interval-valued pythagorean fuzzy sets and TOPSIS-GRA,” Sci. Rep., vol. 14, no. 1, p. 23397, Oct. 2024, doi: 10.1038/s41598-024-73814-w
  25. M. Rahim, F. Amin, K. Shah, T. Abdeljawad, and S. Ahmad, “Some distance measures for pythagorean cubic fuzzy sets: Application selection in optimal treatment for depression and anxiety,” MethodsX, vol. 12, p. 102678, Jun. 2024, doi: 10.1016/j.mex.2024.102678
  26. P. A. Ejegwa, “Pythagorean fuzzy set and its application in career placements based on academic performance using max–min–max composition,” Complex Intell. Syst., vol. 5, no. 2, pp. 165–175, Jun. 2019, doi: 10.1007/s40747-019-0091-6

Last update:

No citation recorded.

Last update:

No citation recorded.