Application of Fuzzy Analytic Hierarchy Process (FAHP) for the Selection of Best Student Award

Application of Fuzzy Analytic Hierarchy Process (FAHP) for the Selection of Best Student Award

Authors

  • Khairu Azlan Abd Aziz College of Computing, Informatics and Media, Universiti Teknologi MARA Perlis Branch, Arau Campus, 02600 Arau, Perlis
  • Mohd Fazril Izhar Mohd Idris UiTM
  • Wan Suhana Wan Daud Institute of Engineering Mathematics, Universiti Malaysia Perlis, 02600 Arau, Perlis
  • Maisarah Mohamad Fauzi College of Computing, Informatics and Media, Universiti Teknologi MARA Perlis Branch, Arau Campus, 02600 Arau, Perlis

DOI:

https://doi.org/10.24191/jcrinn.v8i2.345

Keywords:

Fuzzy Analytic Hierarchy Process, Best Student Award, students' selection

Abstract

The Best Student Award is an award that students, particularly those who perform best, look forward to receiving. It recognized students who have achieved success in both academic and extra-curricular activities during their undergraduate years. However, there are other challenges in the student selection decision such as having trouble in selecting the student with what characteristic features should be considered and prioritized. Hence, the management needs to take the initiative to make students more satisfied with the new process and result. This study aims to determine the important factors and sub-factors needed to select the student for the best student award and rank them according to the most important influence level. To achieve the objective, the Fuzzy Analytic Hierarchy Process (FAHP) has been used to determine ranking factors including Cumulative Grade Point Average (CGPA), soft skills that students possess, discipline, and participation in extra-curricular activities. Each factor comprises three sub-factors. The primary data was collected by distributing questionnaires to six experts, consisting of three (3) academic administrators and three (3) student administrators using Google Forms. Discipline, with a normalized weight of 0.363, is the most important factor in students' selection of excellent students. With a normalized weight of 0.485, the sub-factor teaching and learning process is the most essential for CGPA. With a normalized weight of 0.445, self-confidence is the most important sub-factor under soft skills. Subfactors free from disciplinary action (normalized weight 0.414) are significant for the factor of discipline and committing (normalized weight 0.482) is significant for the factor of extra-curricular activities. Based on this outcome, it shows that Fuzzy AHP is a method that can assist experts to decide under complicated situations and precisely rank all factors and sub-factors. High satisfaction among students with selection decisions will most likely lead to their high and extraordinary fighting spirit.

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References

Amile, M., Sedaghat, M., and Poorhossein, M. (2013). Performance Evaluation of Banks using Fuzzy AHP and TOPSIS, Case study: State-owned Banks, Partially Private and Private Banks in Iran. Caspian Journal of Applied Sciences Research, 2(3), 128–138. http://www.cjasr.com

Ajoi, T. A., Gran, S. S., Kanyan, A., & Lajim, S. F. (2021). An enhanced systematic student performance evaluation based on fuzzy logic approach for selection of best student award. Asian Journal of University Education, 16(4), 10-20. https://doi.org/10.24191/ajue.v16i4.11932

Başaran, S., and Haruna, Y. (2017). Integrating FAHP and TOPSIS to evaluate mobile learning applications for mathematics. Procedia Computer Science, 120, 91–98. https://doi.org/10.1016/j.procs.2017.11.214

Chen, J. F., Hsieh, H. N., & Do, Q. H. (2015). Evaluating teaching performance based on fuzzy AHP and comprehensive evaluation approach. Applied Soft Computing, 28, 100-108. https://doi.org/10.1016/j.asoc.2014.11.050

Fadlina, Sianturi, L. T., Karim, A., Mesran, and Siahaan, A. P. U. (2017). Best Student Selection Using Extended Promethee II Method. International Journal of Recent Trends in Engineering and Research, 3(8), 21-29. https://doi.org/10.23883/ijrter.2017.3382.sk4cv

ICEPS. (2021). Peraturan Akademik Diploma Dan Sarjana Muda UiTM. https://iceps.uitm.edu.my/images/iceps/akademik/info/buku_peraturan_akademik_202101102021.pdf

Jovčić, S., Průša, P., Samson, J., and Lazarević, D. (2019). A fuzzy-Ahp approach to evaluate the criteria of third-party logistics (3pl) service providers. IJTTE: International Journal for Traffic and Transport Engineering, 9(3), 26-34. https://doi.org/10.7708/ijtte.2019.9(3).02

Kabir, G., and Hasin, M. A. A. (2011). Comparative analysis of AHP and fuzzy AHP models for multicriteria inventory classification. International Journal of Fuzzy Logic Systems, 1(1), 1-16. https://www.researchgate.net/publication/267237307

Kahraman, C., Cebeci, U., and Ulukan, Z. (2003). Multi‐criteria supplier selection using fuzzy AHP. Logistics Information Management, 16(6), 382–394. https://doi.org/10.1108/09576050310503367

Norddin, N. I., Ahmad, N., and Yusof, Z. M. (2015). Selecting the best employee of the year using an analytical hierarchy process. Journal of Basic and Applied Scientific Research, 5(11), 72-76, https://www.researchgate.net/publication/287674087

Othman, M. K., Abdul Rahman, N. S. F., Ismail, A., and Saharuddin, A. H. (2020). Factors contributing to the imbalances of cargo flows in Malaysia's large-scale minor ports using a fuzzy analytical hierarchy process (FAHP) approach. Asian Journal of Shipping and Logistics, 36(3), 113–126. https://doi.org/10.1016/j.ajsl.2019.12.012

Rezaei, M., and Ketabi, S. (2016). Ranking the Banks through Performance Evaluation by Integrating Fuzzy AHP and TOPSIS Methods: A Study of Iranian Private Banks. International Journal of Academic Research in Accounting, Finance and Management Sciences, 6(3), 19–30. https://doi.org/10.6007/ijarafms/v6-i3/2148

Sarahintu, M., Tarmudi, Z., and Lepit, A. (2017). 11 - Selection of the Best Pre-Diploma Science Student Using a Fuzzy Approach. Prosiding Kolokium Hal Ehwal Akademik (K-HEA), 2009, 34–40. http://ir.uitm.edu.my/id/eprint/26561/1/PRO_ZAMALI TARMUDI S 17.pdf

Sattar, W., Tony Lim Bin Abdullah, M. R., and Mirzaei, F. (2018). A FAHP approach to select students’ performance assessment criteria in task-based English language teaching. SHS Web of Conferences, 53, 03005. https://doi.org/10.1051/shsconf/20185303005

Sun, C. C. (2010). A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Systems with Applications, 37(12), 7745–7754. https://doi.org/10.1016/j.eswa.2010.04.066

Yadav, R. S., and Singh, V. P. (2011). Modeling academic performance evaluation using soft computing techniques: A fuzzy logic approach. International Journal on Computer Science and Engineering, 3(2), 676-686. http://www.enggjournals.com/ijcse/doc/IJCSE11-03-02-074.pdf

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Published

2023-09-01

How to Cite

Abd Aziz, K. A., Mohd Idris, M. F. I., Wan Daud, W. S., & Mohamad Fauzi, M. (2023). Application of Fuzzy Analytic Hierarchy Process (FAHP) for the Selection of Best Student Award. Journal of Computing Research and Innovation, 8(2), 80–90. https://doi.org/10.24191/jcrinn.v8i2.345

Issue

Section

General Computing

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