Acknowledging the level of relevancy and competitiveness of academic programs is very important to education providers. This in return will benefit students enrolling in the programs. Measuring the level of relevancy and competitiveness of academic programs can be very subjective. In this paper, the application of a quantitative multi criteria decision making method known as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is presented. The relevancy and competitiveness of eleven academic programs offered at the Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam are ranked based on seven criteria using modified Fuzzy TOPSIS. The criteria used in ranking of the academic programs were gathered from a group of twelve decision makers which comprises of top management of the faculty and university understudied. Quantitative ranking process was done based on data provided by the education provider itself. The seven criteria used include percentage of Dean’s Award recipients, percentage of graduates with CGPA > 3.5, percentage of graduate employability, program popularity, percentage of students’ entrance CGPA > 3.0, optimum enrollment based on allocation of places offered and future job demand. The weights of the criteria are determined using Fuzzy Analytical Hierarchy Process (AHP) technique. Modified Fuzzy TOPSIS are then used to rank the academic programs by incorporating the weights of criteria found. Future job demand and percentage of graduate employability are found to be the top two most important criteria used in the ranking process. Bachelor of Science (Hons) Statistics, Bachelor of Information Technology (Hons) Intelligent System Engineering and Bachelor of Science (Hons) Actuarial Science are found to be the top three most competitive programs offered by the institution. Consistency ratio test done indicated that the responses of decision makers are highly consistent thus reliable as reflected in the consistency ratio being < 0.1.
Bellman, R. E. & Zadeh, L. A. (1970). Decision Making in a Fuzzy Environment,
17(4), 141–164.
Bozbura, F. T., Beskese, A. & Kahraman, C. (2007). Prioritization of human capital measurement indicators using fuzzy AHP. Expert Systems with Applications, 32(4), 1100–1112. https://doi.org/10.1016/j.eswa.2006.02.006
Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649–655. https://doi.org/10.1016/0377-2217(95)00300-2.
Harliza, M. H., Daud, M. & Hashimah, N. S. (2013). Solving decision making
Problems using fuzzy numbers with area dominance approach. AIP Conference proceedings of National Symposium on Mathematical Sciences, 18th December 2012, Putrajaya, Wilayah Persekutuan.
Kalaimagal., R. and Norizan, M. Y. (2012). Employment issues among Malaysian information and communication technology (ICT) graduates: A case study. African Journal of Business Management. 6 (16), pp. 5615-5621.
Hanif, M. H., Sulaiman, N. H. & Mohamad, D. (2013). Solving Decision Making
Problems using Fuzzy Numbers with area dominance approach. AIP Conference Proceedings. 1522, p 237-244.
Hashimah, N. S. & Daud, M. (2017). Extended FTOPSIS with Distance
and set Therotic –Based Similarity Measure. Indonesian Journal of Electrical Engineering and Computer Science. 9 (2), February 2018, pp. 387~394 ISSN: 2502-4752, DOI: 10.11591/ijeecs.v9.i2.pp387-394.
Hashimah, N. S., Daud, M., Jamilah, M. S. & Aniza, S. A. (2018). Extended
FTOPSIS with distance and set theoretic-based similarity measure. Indonesian Journal of Electrical Engineering and Computer Science, 9(2), 387-394.
Sulaiman, N.H. & Mohamad, D. (2013). A set theoretic similarity measure for fuzzy
soft sets and its applications in group decision making. AIP Conference Proceedings. 1522, p 229-236.
Prascevic, N., & Prascevic, Ž. (2016). Application of fuzzy AHP method based on
eigenvalues for decision making in construction industry. 1. https://doi.org/10.17559/TV-20140212113942.
Suraya, B, Norsalawati, W. N. I. (2017). Integration of STEM
Education in Malaysia and Why to STEAM. International Journal of Academic Research in Business and Social Sciences. 7(6).
Syahanim, M. S., Zarina, S., Hairulliza, M. J. (2013). Analysis
of Research in Programming Teaching Tools: An Initial Review. Procedia - Social and Behavioral Sciences. 103. pp 127 – 135. 13th International Educational Technology Conference.
Vahdani, B., Mousavi, S. M., & Tavakkoli-Moghaddam, R. (2011). Group decision making based on novel fuzzy modified TOPSIS method. Applied Mathematical Modelling, 35(9), 4257–4269. https://doi.org/10.1016/j.apm.2011.02.040
Zadeh, L. A. (1965). Fuzzy sets *. University of California, Berkeley, California.
In-Text Citation: (Dom, Hasan, Shahidin, & Apandi, 2019)
To Cite this Article: Dom, R. M., Hasan, H., Shahidin, A. M., & Apandi, N. A. (2019). Fuzzy Tops is Ranking of Academic Programs’ Competitiveness. International Journal of Academic Research in Business and Social Sciences, 9(13), 319–328.
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