Research Article
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Year 2023, Volume: 11 Issue: 1, 11 - 18, 30.01.2023
https://doi.org/10.21541/apjess.1143964

Abstract

References

  • [1] Saaty, T. L., (1986), Axiomatic Foundation of the Analytic Hierarchy Process, Management Science, 32 (7), 841-855.
  • [2] Keskenler M.F. ve Keskenler E.F., (2017). Bulanık Mantığın Tarihi Gelişimi, Takvim-i Vekayi, 5(1),1-10.
  • [3] Torra, V., (2010). Hesitant Fuzzy Sets, International Journal of Intelligent Systems, 25,529–539.
  • [4] Rodriguez, R.M., Martinez, L. ve Herrera, F., (2013). A Group Decision Making Model Dealing with Comparative Linguistic Expressions Based on Hesitant Fuzzy Linguistic Term Sets, Information Sciences, 241,28-42.
  • [5] Kutlu Gündoğdu, F., Kahraman, C. (2019). A Novel Fuzzy TOPSIS Method Using Emerging Interval-Valued Spherical Fuzzy Sets. Engineering Applications of Artificial Intelligence 85, 307–323.
  • [6] Zadeh, L. A. (1975). Fuzzy Logic and Approximate Reasoning. Synthese, 30 (3-4), 407-428.
  • [7] Bojadziev G., Bojadziev M. (1998). Fuzzy Sets, Fuzzy Logic, Applications, World Scientific, London.
  • [8] Ertuğrul, İ., Tuş, A. (2007). Interactive Fuzzy Linear Programming and an Application Samplat a Textile Firm. Fuzzy Optimization and Decision Making, 6(1), 29-49.
  • [9] Zimmermann, H. J. (1992). Methods and Applications of Fuzzy Mathematical Programming. In an Introduction to Fuzzy Logic Applications n Intelligent Systems 97-120. Springer, Boston, MA.
  • [10] Wu, D., Mendel, J. M. (2008). A Vector Similarity Measure for Linguistic Approximation: Interval Type-2 and Type-1 Fuzzy Sets. Information Sciences, 178(2), 381-402.
  • [11] Castillo, O., Melin, P., Kacprzyk, J., Pedrycz, W. (2007). Type-2 Fuzzy Logic: Theory and Applications. IEEE International Conference on Granular Computing (GRC 2007),145-147. IEEE.
  • [12] Xu, Z. (2014). Hesitant Fuzzy Sets Theory (Vol. 314). Cham: Springer International Publishing.
  • [13] Torra, V. (2010). Hesitant Fuzzy Sets. International Journal of Intelligent Systems, 25(6), 529- 539.
  • [14] Tepe, S., Kaya, I. (2019) A Fuzzy-Based Risk Assessment Model for Evaluations of Hazards with a Real-Case Study, Human and Ecological Risk Assessment: An International Journal, 26(2), 512-537, DOI: 10.1080/10807039.2018.1521262
  • [15] Rodriguez, R. M., Martinez, L., Herrera, F. (2011). Hesitant Fuzzy Linguistic Term Sets for Decision Making. IEEE Transactions on Fuzzy Systems, 20(1), 109-119.
  • [16] Wang, F., Li, X., Chen, X. (2014). Hesitant Fuzzy Soft Set and Its Applications in Multicriteria Decision Making. Journal of Applied Mathematics.
  • [17] Bustince, H., Burillo, P. (1996). Vague Sets are Intuitionistic Fuzzy Sets. Fuzzy Sets and Systems,79(3), 403-405.
  • [18] Mondal, T. K., Samanta, S. K. (1999). Topology of Interval-Valued Fuzzy Sets. Indian Journal of Pure and Applied Mathematics, 30, 23-29.
  • [19] Tepe, S., Kaya, I. (2018). A New Risk Assessment Methodology by Using Pythagorean Fuzzy Analytic Hierarchy Process. 12th International Conference on Challenges in Industrial Engineering and Operations Management, 110.
  • [20] Yager, R. R. (2013). Pythagorean Fuzzy Subsets. In 2013 Joint IFSA World Congress And NAFIPS Annual Meeting (IFSA/NAFIPS) , 57-61. IEEE.
  • [21] Lee, A. H., Chen, W. C., Chang, C. J. (2008). A Fuzzy AHP and BSC Approach for Evaluating Performance of IT Department in the Manufacturing Industry in Taiwan. Expert Systems with Applications, 34(1), 96-107.
  • [22] Kumar, N. V., Ganesh, L. S. (1996). An Empirical Analysis of the Use of the Analytic Hierarchy Process for Estimating Membership Values in a Fuzzy Set. Fuzzy Sets and Systems, 82(1), 1-16.
  • [23] Kahraman C., Kaya I. (2010). A Fuzzy Multicriteria Methodology for Selection Among Energy Alternatives. Expert Systems with Applications, 37(9), 6270–6281
  • [24] Kutlu Gündoğdu, F., Kahraman, C. (2020). A Novel Spherical Fuzzy Analytic Hierarchy Process and Its Renewable Energy Application. Soft Computing 24, 4607–4621 https://doi.org/10.1007/s00500-019-04222-w
  • [25] Ilbahar, E., Karaşan, A., Cebi, S., Kahraman, C. (2018). A Novel Approach to Risk Assessment for Occupational Health and Safety Using Pythagorean Fuzzy AHP Fuzzy Inference System. Safety Science, 103, 124-136.

Coding Program Selection Using Spherical Fuzzy Analytic Hierarchy and Pythagorean Fuzzy Analytic Hierarchy Processes

Year 2023, Volume: 11 Issue: 1, 11 - 18, 30.01.2023
https://doi.org/10.21541/apjess.1143964

Abstract

Today, coding is not a field that belongs only to software developers; it has become a field of interest to many people from different professions. The coding education designed for elementary school level resulting from the changes made in the curriculum has led to teaching analytical thinking to children. Deciding the most suitable software for children among all the options is an important issue. This paper aims to extend the classical Analytical Hierarchy Process (AHP) and look at the spherical fuzzy analytical hierarchy (SF-AHP) method to show its applicability to the problems of coding software program selection for children through a comparative analysis using Pythagorean AHP (PF-AHP). After performing the analysis by using the proposed method, it was found that technological facilities, diversity, cost and environmental conditions were the most critical factors according to SF-AHP and PF-AHP methodologies. According to these criteria, the educational programming platform ‘Tynker’ was determined to be the best alternative using both these methods.

References

  • [1] Saaty, T. L., (1986), Axiomatic Foundation of the Analytic Hierarchy Process, Management Science, 32 (7), 841-855.
  • [2] Keskenler M.F. ve Keskenler E.F., (2017). Bulanık Mantığın Tarihi Gelişimi, Takvim-i Vekayi, 5(1),1-10.
  • [3] Torra, V., (2010). Hesitant Fuzzy Sets, International Journal of Intelligent Systems, 25,529–539.
  • [4] Rodriguez, R.M., Martinez, L. ve Herrera, F., (2013). A Group Decision Making Model Dealing with Comparative Linguistic Expressions Based on Hesitant Fuzzy Linguistic Term Sets, Information Sciences, 241,28-42.
  • [5] Kutlu Gündoğdu, F., Kahraman, C. (2019). A Novel Fuzzy TOPSIS Method Using Emerging Interval-Valued Spherical Fuzzy Sets. Engineering Applications of Artificial Intelligence 85, 307–323.
  • [6] Zadeh, L. A. (1975). Fuzzy Logic and Approximate Reasoning. Synthese, 30 (3-4), 407-428.
  • [7] Bojadziev G., Bojadziev M. (1998). Fuzzy Sets, Fuzzy Logic, Applications, World Scientific, London.
  • [8] Ertuğrul, İ., Tuş, A. (2007). Interactive Fuzzy Linear Programming and an Application Samplat a Textile Firm. Fuzzy Optimization and Decision Making, 6(1), 29-49.
  • [9] Zimmermann, H. J. (1992). Methods and Applications of Fuzzy Mathematical Programming. In an Introduction to Fuzzy Logic Applications n Intelligent Systems 97-120. Springer, Boston, MA.
  • [10] Wu, D., Mendel, J. M. (2008). A Vector Similarity Measure for Linguistic Approximation: Interval Type-2 and Type-1 Fuzzy Sets. Information Sciences, 178(2), 381-402.
  • [11] Castillo, O., Melin, P., Kacprzyk, J., Pedrycz, W. (2007). Type-2 Fuzzy Logic: Theory and Applications. IEEE International Conference on Granular Computing (GRC 2007),145-147. IEEE.
  • [12] Xu, Z. (2014). Hesitant Fuzzy Sets Theory (Vol. 314). Cham: Springer International Publishing.
  • [13] Torra, V. (2010). Hesitant Fuzzy Sets. International Journal of Intelligent Systems, 25(6), 529- 539.
  • [14] Tepe, S., Kaya, I. (2019) A Fuzzy-Based Risk Assessment Model for Evaluations of Hazards with a Real-Case Study, Human and Ecological Risk Assessment: An International Journal, 26(2), 512-537, DOI: 10.1080/10807039.2018.1521262
  • [15] Rodriguez, R. M., Martinez, L., Herrera, F. (2011). Hesitant Fuzzy Linguistic Term Sets for Decision Making. IEEE Transactions on Fuzzy Systems, 20(1), 109-119.
  • [16] Wang, F., Li, X., Chen, X. (2014). Hesitant Fuzzy Soft Set and Its Applications in Multicriteria Decision Making. Journal of Applied Mathematics.
  • [17] Bustince, H., Burillo, P. (1996). Vague Sets are Intuitionistic Fuzzy Sets. Fuzzy Sets and Systems,79(3), 403-405.
  • [18] Mondal, T. K., Samanta, S. K. (1999). Topology of Interval-Valued Fuzzy Sets. Indian Journal of Pure and Applied Mathematics, 30, 23-29.
  • [19] Tepe, S., Kaya, I. (2018). A New Risk Assessment Methodology by Using Pythagorean Fuzzy Analytic Hierarchy Process. 12th International Conference on Challenges in Industrial Engineering and Operations Management, 110.
  • [20] Yager, R. R. (2013). Pythagorean Fuzzy Subsets. In 2013 Joint IFSA World Congress And NAFIPS Annual Meeting (IFSA/NAFIPS) , 57-61. IEEE.
  • [21] Lee, A. H., Chen, W. C., Chang, C. J. (2008). A Fuzzy AHP and BSC Approach for Evaluating Performance of IT Department in the Manufacturing Industry in Taiwan. Expert Systems with Applications, 34(1), 96-107.
  • [22] Kumar, N. V., Ganesh, L. S. (1996). An Empirical Analysis of the Use of the Analytic Hierarchy Process for Estimating Membership Values in a Fuzzy Set. Fuzzy Sets and Systems, 82(1), 1-16.
  • [23] Kahraman C., Kaya I. (2010). A Fuzzy Multicriteria Methodology for Selection Among Energy Alternatives. Expert Systems with Applications, 37(9), 6270–6281
  • [24] Kutlu Gündoğdu, F., Kahraman, C. (2020). A Novel Spherical Fuzzy Analytic Hierarchy Process and Its Renewable Energy Application. Soft Computing 24, 4607–4621 https://doi.org/10.1007/s00500-019-04222-w
  • [25] Ilbahar, E., Karaşan, A., Cebi, S., Kahraman, C. (2018). A Novel Approach to Risk Assessment for Occupational Health and Safety Using Pythagorean Fuzzy AHP Fuzzy Inference System. Safety Science, 103, 124-136.
There are 25 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Research Articles
Authors

Serap Tepe 0000-0002-9723-6049

Publication Date January 30, 2023
Submission Date July 14, 2022
Published in Issue Year 2023 Volume: 11 Issue: 1

Cite

IEEE S. Tepe, “Coding Program Selection Using Spherical Fuzzy Analytic Hierarchy and Pythagorean Fuzzy Analytic Hierarchy Processes”, APJESS, vol. 11, no. 1, pp. 11–18, 2023, doi: 10.21541/apjess.1143964.

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