Hesitant Fuzzy Linguistic Term Sets with Fuzzy Grid Partition in Determining the Best Lecturer

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    Decision-making on conditions that involve many alternatives, many criteria, and many judgments is a difficult thing to do. The difficulty is coupled with assessors who sometimes make decisions in hesitant, unclear, and inconsistent circumstances and each person can provide different judgments. One of the methods that can be used is Hesitant Fuzzy Linguistic Term Sets which is the development of Fuzzy Sets that can make decisions by using Hesitant Fuzzy Sets. Hesitant linguistic term has been introduced for capturing the human way of reasoning using linguistic expressions involving different levels of precision. The integration of Hesitant Fuzzy Linguistic Term Sets with Fuzzy Grid Partition will enhance the ability in the decision making process. This research will discuss the use of Hesitant Fuzzy Linguistic Term Sets method and Fuzzy Grid Partition for best lecturers determination.

     


  • Keywords


    Decision-Making, Hesitant Fuzzy Sets, Fuzzy Sets, Hesitant Fuzzy Linguistic Term Sets

  • References


      [1] H. Wang, Z. Xu, and X.-J. Zeng, “Hesitant fuzzy linguistic term sets for linguistic decision making: Current developments, issues and challenges,” Inf. Fusion, vol. 43, pp. 1–12, Sep. 2018.

      [2] D. Abdullah, Tulus, S. Suwilo, S. Efendi, Hartono, and C. I. Erliana, “A Slack-Based Measures for Improving the Efficiency Performance of Departments in Universitas Malikussaleh,” Int. J. Eng. Technol., vol. 7, no. 2, pp. 491–494, Apr. 2018.

      [3] D. Abdullah, Tulus, S. Suwilo, S. Effendi, and Hartono, “DEA Optimization with Neural Network in Benchmarking Process,” IOP Conf. Ser. Mater. Sci. Eng., vol. 288, p. 012041, Jan. 2018.

      [4] F. Herrera and E. Herrera-Viedma, “Linguistic decision analysis: steps for solving decision problems under linguistic information,” Fuzzy Sets Syst., vol. 115, no. 1, pp. 67–82, Oct. 2000.

      [5] Hartono, O. S. Sitompul, Tulus, and E. B. Nababan, “Optimization Model of K-Means Clustering Using Artificial Neural Networks to Handle Class Imbalance Problem,” IOP Conf. Ser. Mater. Sci. Eng., vol. 288, p. 012075, Jan. 2018.

      [6] Hartono, D. Abdullah, and A. S. Ahmar, “A New Diversity Technique for Imbalance Learning Ensembles,” Int. J. Eng. Technol., vol. 7, no. 2, pp. 478–483, Apr. 2018.

      [7] H. Hartono, O. S. Sitompul, T. Tulus, and E. B. Nababan, “Biased support vector machine and weighted-smote in handling class imbalance problem,” Int. J. Adv. Intell. Inform., vol. 4, no. 1, pp. 21–27, Apr. 2018.

      [8] C. I. Erliana and D. Abdullah, “Application of The MODAPTS Method with Innovative Solutions in The Cement Packing Process,” Int. J. Eng. Technol., vol. 7, no. 2, pp. 470–473, Apr. 2018.

      [9] D. Pontan et al., “Effect of The Building Maintenance and Resource Management Through User Satisfaction of Maintenance,” Int. J. Eng. Technol., vol. 7, no. 2, pp. 462–465, Apr. 2018.

      [10] D. Napitupulu et al., “Analysis of Student Satisfaction Toward Quality of Service Facility,” J. Phys. Conf. Ser., vol. 954, p. 012019, Jan. 2018.

      [11] L. A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning—I,” Inf. Sci., vol. 8, no. 3, pp. 199–249, Jan. 1975.

      [12] A. S. Ahmar et al., “Lecturers’ Understanding on Indexing Databases of SINTA, DOAJ, Google Scholar, SCOPUS, and Web of Science: A Study of Indonesians,” J. Phys. Conf. Ser., vol. 954, p. 012026, Jan. 2018.

      [13] P. harliana and R. Rahim, “Comparative Analysis of Membership Function on Mamdani Fuzzy Inference System for Decision Making,” J. Phys. Conf. Ser., vol. 930, no. 1, p. 012029, 2017.

      [14] D. Siregar, D. Arisandi, A. Usman, D. Irwan, and R. Rahim, “Research of Simple Multi-Attribute Rating Technique for Decision Support,” J. Phys. Conf. Ser., vol. 930, no. 1, p. 012015, 2017.

      [15] R. Degani and G. Bortolan, “The Problem of Linguistic Approximation in Clinical Decision Making,” p. 20.

      [16] D. Dubois, H. M. Prade, and R. R. Yager, Readings in fuzzy sets for intelligent systems. Morgan Kaufmann, , Elsevier Inc, 1993.

      [17] H. Hartono, “Optimization of Tsukamoto Fuzzy Inference System using Fuzzy Grid Partition,” Int. J. Comput. Sci. Netw., vol. 5, no. 5, pp. 786–791, Oct. 2016.

      [18] L. Martínez, D. Ruan, and F. Herrera, “Computing with Words in Decision support Systems: An overview on Models and Applications,” Int. J. Comput. Intell. Syst., vol. 3, no. 4, pp. 382–395, Oct. 2010.

      [19] R. M. Rodríguez, Á. Labella, and L. Martínez, “An Overview on Fuzzy Modelling of Complex Linguistic Preferences in Decision Making,” Int. J. Comput. Intell. Syst., vol. 9, no. sup1, pp. 81–94, May 2016.

      [20] B. Zhang, H. Liang, and G. Zhang, “Reaching a consensus with minimum adjustment in MAGDM with hesitant fuzzy linguistic term sets,” Inf. Fusion, vol. 42, pp. 12–23, Jul. 2018.

      [21] “Hesitant Fuzzy Linguistic Term Sets for Decision Making - IEEE Journals & Magazine.” [Online]. Available: https://ieeexplore.ieee.org/document/6030926/. [Accessed: 23-Apr-2018].

      [22] H. Liu and R. M. Rodríguez, “A fuzzy envelope for hesitant fuzzy linguistic term set and its application to multicriteria decision making,” Inf. Sci., vol. 258, pp. 220–238, Feb. 2014.

      [23] M. Ashtiani and M. A. Azgomi, “A hesitant fuzzy model of computational trust considering hesitancy, vagueness and uncertainty,” Appl. Soft Comput., vol. 42, pp. 18–37, May 2016.


 

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Article ID: 12322
 
DOI: 10.14419/ijet.v7i2.3.12322




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