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IJMLC 2020 Vol.10(2): 339-345 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.2.940

Implementation of Fuzzy Logic Technique in a Decision Support Tool: Basis for Choosing Appropriate Career Path

Benilda Eleonor V. Comendador, Wilmarie Faye C. Becbec, and John Rez P. de Guzman

Abstract—The authors implemented a fuzzy logic technique to develop a mobile based decision support tool called Generalized STEM-College Aptitude Test (GSTEM-CAT). It is an application that can recommend to the college applicant what appropriate university program can be enrolled based on ones’ personality type and knowledge strength. Moreover, the students may enjoy GSTEM-CAT application because they see graphical objects associated to personality type test. The developed tool was evaluated by the respondents and based on the evaluation results, the software satisfied its implied functions and is functional, usable and reliable. Hence, it can be already used by the students and school administrators to assess and recommend set of college program that the student may take up, therefore providing opportunities for successful program completion.

Index Terms—Decision support tool, fuzzy control system, fuzzy logic technique, holland’s code.

The authors are with the Polytechnic University of the Philippines, College of Computer and Information Sciences, Sta. Mesa, Manila, Philippines (e-mail: bennycomendador@yahoo.com, wilmariefaye@gmail.com, johnrezdeguzman9123@gmail.com).

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Cite: Benilda Eleonor V. Comendador, Wilmarie Faye C. Becbec, and John Rez P. de Guzman, "Implementation of Fuzzy Logic Technique in a Decision Support Tool: Basis for Choosing Appropriate Career Path," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 339-345, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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