Decision support system for selection of major concentration using fuzzy logic
DOI:
https://doi.org/10.35335/f1j7ys60Keywords:
Fuzzy Logic, Decision Support System, Selection of Major Concentration, Student Interests, Academic AbilityAbstract
The choice of major concentration at the tertiary level is an important stage in one's educational development, involving various subjective factors such as interests, abilities, career goals, and subject preferences. To overcome the complexity of this process, we propose the development of a Fuzzy Logic-based Decision Support System (SPK) that can provide recommendations for major concentrations that are more in line with student profiles. In this study, we designed and implemented a DSS model that uses the Fuzzy Logic method to overcome uncertainty and ambiguity in concentration selection. The membership function that has been defined describes the degree of membership in each relevant set. Fuzzy rules are formed based on domain knowledge and historical data, and are applied in the inference process to produce recommendations for major concentrations. Fuzzy Logic in building a Decision Support System that can improve the process of selecting major concentrationsReferences
Altbach, P. G., Reisberg, L., & Rumbley, L. E. (2019). Trends in global higher education: Tracking an academic revolution (Vol. 22). Brill.
Bailey, T. R., Jaggars, S. S., & Jenkins, D. (2015). Redesigning America’s community colleges: A clearer path to student success. Harvard University Press.
Barnett, R. (1997). Higher education: A critical business. McGraw-Hill Education (UK).
Benzing, C., Chu, H. M., & Kara, O. (2009). Entrepreneurs in Turkey: A factor analysis of motivations, success factors, and problems. Journal of Small Business Management, 47(1), 58–91.
Craig, P., Dieppe, P., Macintyre, S., Michie, S., Nazareth, I., & Petticrew, M. (2008). Developing and evaluating complex interventions: the new Medical Research Council guidance. Bmj, 337.
Cullen, A. C., & Frey, H. C. (1999). Probabilistic techniques in exposure assessment: a handbook for dealing with variability and uncertainty in models and inputs. Springer Science & Business.
Hemsley‐Brown, J., & Oplatka, I. (2006). Universities in a competitive global marketplace: A systematic review of the literature on higher education marketing. International Journal of Public Sector Management, 19(4), 316–338.
Heo, Y. (2011). Bayesian calibration of building energy models for energy retrofit decision-making under uncertainty. Georgia Institute of Technology.
Kann, A., & Weyant, J. P. (2000). Approaches for performing uncertainty analysis in large-scale energy/economic policy models. Environmental Modeling & Assessment, 5, 29–46.
Keith, A. J., & Ahner, D. K. (2021). A survey of decision making and optimization under uncertainty. Annals of Operations Research, 300(2), 319–353.
Lent, R. W., Brown, S. D., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45(1), 79–122.
Maltese, A. V, & Tai, R. H. (2011). Pipeline persistence: Examining the association of educational experiences with earned degrees in STEM among US students. Science Education, 95(5), 877–907.
Mitra, S., & Pal, S. K. (2005). Fuzzy sets in pattern recognition and machine intelligence. Fuzzy Sets and Systems, 156(3), 381–386.
Organization, W. H. (2006). Air quality guidelines: global update 2005: particulate matter, ozone, nitrogen dioxide, and sulfur dioxide. World Health Organization.
Salaki, R. J., Kawet, C. R., Manoppo, R., & Tumimomor, F. (2015). Decision support systems major selection vocational high school in using fuzzy logic android-based. Int. Conf. Electr. Eng. Informatics, Its Educ.
Wang, X. (2013). Why students choose STEM majors: Motivation, high school learning, and postsecondary context of support. American Educational Research Journal, 50(5), 1081–1121.
Willging, P. A., & Johnson, S. D. (2009). Factors that influence students’ decision to dropout of online courses. Journal of Asynchronous Learning Networks, 13(3), 115–127.
Zadeh, Lotfi A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 90(2), 111–127.
Zadeh, Lotfi Asker, Klir, G. J., & Yuan, B. (1996). Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers (Vol. 6). World scientific.
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