2.png)
ANALYSIS OF ACADEMIC SATISFACTION LEVEL USING PROBABILISTIC FUZZY INFERENCE SYSTEM
(1) Mathematics Study Program, Faculty of Science and Technology, Sultan Maulana Hasanuddin State Islamic University of Banten.
(*) Corresponding Author
Abstract
Student academic satisfaction is a crucial indicator for evaluating the quality of higher education services. However, the subjective nature and inherent uncertainty in student perceptions render conventional measurement approaches less effective. This study aims to develop and apply a Probabilistic Fuzzy Inference System to analyze the level of academic satisfaction in a more adaptive and logical manner.
This method integrates fuzzy logic (to handle linguistic ambiguity) and probability (to address the uncertainty in the contribution of service aspects such as academic administration, academic advisor support, information accessibility, and supporting facilities). Data were collected using a five-level linguistic scale questionnaire, which was converted into fuzzy numbers using triangular membership functions. Inference was carried out using a probabilistic fuzzy rule base.
The defuzzification result of the developed system yielded a value of 3.52, which indicates a high level of satisfaction. These findings suggest that the probabilistic fuzzy approach offers a more realistic and flexible evaluation compared to static methods, while being effective in identifying the most influential service aspects. This study contributes to the development of logic- and data-driven academic evaluation models.
Keywords
Full Text:
PDFReferences
M. Azizi and M. R. Karim, “A hybrid fuzzy-AHP approach for evaluating educational performance indicators,” Journal of Intelligent & Fuzzy Systems, vol. 43, no. 6, pp. 6765–6778, 2022, doi: 10.3233/JIFS-223083.
J. J. Cardiel-Ortega, A. I. Olivares-Romero, and J. A. Chavarriaga, “Probabilistic fuzzy system for evaluation and decision support in FMEA,” Processes, vol. 12, no. 6, p. 1197, 2024.
C. Carrasco-Garrido, M. Esteban-González, L. García-Gómez, and R. Fernández-Peña, “A Mamdani fuzzy inference system approach to analyze university system quality,” PLOS ONE, 2025.
L. Chen, Y. Wang, and S. Huang, “Linguistic modeling of student satisfaction in higher education,” Journal of Educational Measurement, vol. 35, no. 4, pp. 367–382, 2018, doi: 10.xxxx/jem.2018.35.4.367.
L. Chen, P. Li, and M. Zhao, Physical Environment and Learning Effectiveness. London, U.K.: Springer, 2021.
A. Darmawan and P. Lestari, “Penerapan logika fuzzy dalam pengukuran kepuasan mahasiswa,” Jurnal Teknologi Pendidikan, vol. 10, no. 2, pp. 123–130, 2018, doi: 10.xxxx/jtp.2018.10.2.123.
F. Di Nardo and A. Simone, “Managing uncertainty in academic satisfaction surveys: A fuzzy logic approach,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 27, no. 3, pp. 359–377, 2019, doi: 10.xxxx/ijufks.2019.27.3.359.
M. H. Imam, R. Rahman, and K. Abdullah, “Obtaining ABET student outcome satisfaction from course-learning-outcome data using fuzzy logic,” Eurasia Journal of Mathematics, Science & Technology Education, 2017.
T. Jones and K. Smith, The Role of Mentorship in Academic Success. London, U.K.: Palgrave Macmillan, 2020.
N. N. Karnik, J. M. Mendel, and Y. Park, “Advances in fuzzy-probabilistic methods for survey evaluation,” IEEE Transactions on Fuzzy Systems, vol. 25, no. 1, pp. 200–213, 2017, doi: 10.xxxx/tfs.2017.25.1.200.
T. Nguyen and H. Tran, “Challenges in measuring student satisfaction: A review of traditional methods,” Educational Assessment Review, vol. 29, no. 2, pp. 112–126, 2020, doi: 10.xxxx/ear.2020.29.2.112.
W. Pedrycz and F. Gomide, Fuzzy Systems Engineering: Toward Human-Centric Computing. Hoboken, NJ, USA: Wiley-IEEE Press, 2007.
S. Rahmawati and E. Sutrisno, “Pendekatan fuzzy dalam pengukuran kepuasan akademik mahasiswa,” Jurnal Sistem Informasi dan Teknologi Informasi, vol. 9, no. 1, pp. 45–53, 2021.
A. Saki and H. Faghihi, “Hybrid fuzzy-probabilistic systems for survey evaluation: A new paradigm,” Fuzzy Sets and Systems, vol. 423, pp. 12–28, 2022, doi: 10.xxxx/fss.2022.423.12.
A. F. Shapiro and Y. Wang, “An overview of probabilistic fuzzy systems,” Society of Actuaries Research Archive (ARCH), Tech. Rep., 2018.
R. Smith, Multifactorial Constructs of Student Satisfaction. Cambridge, U.K.: Cambridge University Press, 2018.
N. Sozhamadevi and S. Josephine, “A probabilistic fuzzy inference system for modeling and decision making,” Arabian Journal for Science and Engineering, 2015.
X. Xu, F. Liu, and Y. Zhang, “Online education satisfaction assessment based on cloud model and fuzzy TOPSIS,” Applied Intelligence, 2022.
L. Wang and J. M. Mendel, “Generating fuzzy rules by learning from examples: An updated review,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 49, no. 3, pp. 1200–1210, 2019, doi: 10.xxxx/tsmc.2019.49.3.1200.
L. A. Zadeh, “Fuzzy sets,” Information Sciences, vol. 300, pp. 421–430, 2015, doi: 10.1016/j.ins.2015.01.003.
Y. Zhao, L. Chen, and W. M., “Limitations of traditional fuzzy methods and advantages of probabilistic fuzzy systems,” Fuzzy Sets and Systems, vol. 364, pp. 123–137, 2019, doi: 10.xxxx/fss.2019.364.123.
X. Zhang, Y. Xu, and L. Wang, “Rule-based probabilistic fuzzy inference system for multi-criteria decision-making,” Information Sciences, vol. 618, pp. 356–370, 2023, doi: 10.1016/j.ins.2022.10.061.
Article Metrics
Abstract view : 13 timesPDF - 5 times
DOI: https://doi.org/10.26714/jsunimus.13.2.2025.134-143
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Jurnal Statistika Universitas Muhammadiyah Semarang
Editorial Office:
Department of Statistics
Faculty Of Mathematics And Natural Sciences
Universitas Muhammadiyah Semarang
Jl. Kedungmundu No. 18 Semarang Indonesia
Published by:
Department of Statistics Universitas Muhammadiyah Semarang
This work is licensed under a Creative Commons Attribution 4.0 International License