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
E. Di Nardo and R. Simone, “A model-based fuzzy analysis of questionnaires,” Stat. Methods Appl. 2018 282, vol. 28, no. 2, pp. 187–215, Nov. 2018, doi: 10.1007/S10260-018-00443-9.
Y. Liu, T. Bao, D. Zhao, H. Sang, and B. Fu, “Evaluation of Student-Perceived Service Quality in Higher Education for Sustainable Development: A Fuzzy TODIM-ERA Method,” Sustain. 2022, Vol. 14, Page 4761, vol. 14, no. 8, p. 4761, Apr. 2022, doi: 10.3390/SU14084761.
A. Saki and U. Faghihi, “A Fundamental Probabilistic Fuzzy Logic Framework Suitable for Causal Reasoning,” May 2022, Accessed: Jan. 06, 2026. [Online]. Available: https://arxiv.org/pdf/2205.15016
K. Rudnik and A. M. Deptuła, “System with probabilistic fuzzy knowledge base and parametric inference operators in risk assessment of innovative projects,” Expert Syst. Appl., vol. 42, no. 17–18, pp. 6365–6379, Oct. 2015, doi: 10.1016/J.ESWA.2015.04.025.
J. J. Cardiel-Ortega and R. Baeza-Serrato, “Probabilistic Fuzzy System for Evaluation and Classification in Failure Mode and Effect Analysis,” Process. 2024, Vol. 12, Page 1197, vol. 12, no. 6, p. 1197, Jun. 2024, doi: 10.3390/PR12061197.
L. Abdullah, “Fuzzy Multi Criteria Decision Making and its Applications: A Brief Review of Category,” Procedia - Soc. Behav. Sci., vol. 97, pp. 131–136, Nov. 2013, doi: 10.1016/J.SBSPRO.2013.10.213.
A. Haris and N. Asnawi, “Quality Perception Of Islamic Higher Education Institution Services: Experience Of International Students,” Ulul Albab J. Stud. Islam, vol. 21, no. 1, pp. 188–212, Jul. 2020, doi: 10.18860/UA.V21I1.9942.
M. H. Imam, I. A. Tasadduq, A. R. Ahmad, and F. Aldosari, “Obtaining ABET student outcome satisfaction from course learning outcome data using fuzzy logic,” Eurasia J. Math. Sci. Technol. Educ., vol. 13, no. 7, pp. 3069–3081, 2017, doi: 10.12973/EURASIA.2017.00705A.
X. Xu, J. Xie, H. Wang, and M. Lin, “Online education satisfaction assessment based on cloud model and fuzzy TOPSIS,” Appl. Intell. (Dordrecht, Netherlands), vol. 52, no. 12, p. 13659, Sep. 2022, doi: 10.1007/S10489-022-03289-7.
C. Carrasco-Garrido, B. M. Moreno-Cabezali, and A. Martínez Raya, “New perspectives on university quality assessment: A Mamdani Fuzzy Inference System approach,” PLoS One, vol. 20, no. 5, p. e0321013, May 2025, doi: 10.1371/JOURNAL.PONE.0321013.
A. F. Shapiro and D. Wang, “An Overview of Probabilistic Fuzzy Systems -- Some Preliminary Observations”.
J. Ward Creswell, “Research design : qualitative, quantitative, and mixed methods approaches /,” SAGE Publ. Inc., pp. 3–23, 2018, Accessed: Jan. 06, 2026. [Online]. Available: https://books.google.com/books/about/Research_Design.html?hl=id&id=s4ViswEACAAJ
H.-J. Zimmermann, “Fuzzy Set Theory—and Its Applications,” Fuzzy Set Theory—and Its Appl., 2001, doi: 10.1007/978-94-010-0646-0.
R. Montes, C. Zuheros, J. M. Morales, N. Zermeño, J. Duran, and F. Herrera, “Design and consensus content validity of the questionnaire for b-learning education: A 2-Tuple Fuzzy Linguistic Delphi based Decision Support Tool,” Appl. Soft Comput., vol. 147, Feb. 2024, doi: 10.1016/j.asoc.2023.110755.
W. Pedrycz and F. Gomide, “Fuzzy Systems Engineering: Toward Human-Centric Computing,” IEEE eBooks IEEE Xplore. Accessed: Jan. 06, 2026. [Online]. Available: https://ieeexplore.ieee.org/book/5201525
A. Gupta, A. Nanda, S. Sawhney, and A. Gupta, “Examining the Impact of Alumni Mentoring on Student Satisfaction and Engagement in Higher Technical Education,” J. Eng. Educ. Transform., vol. 38, no. Special Issue, pp. 412–419, Jan. 2025, doi: 10.16920/JEET/2025/V38IS2/25050.
N. Makaremi, S. Yildirim, G. T. Morgan, M. F. Touchie, J. A. Jakubiec, and J. B. Robinson, “Impact of classroom environment on student wellbeing in higher education: Review and future directions,” Build. Environ., vol. 265, p. 111958, Nov. 2024, doi: 10.1016/J.BUILDENV.2024.111958.
Article Metrics
Abstract view : 212 timesPDF - 33 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
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