Modelling Perceptive-Based Information (Words) For Decision Support System
Elissa Nadia Madi1, Binyamin Yusoff2

1Elissa Nadia Madi, Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin, Besut Campus, Terengganu, Malaysia.
2Binyamin Yusoff, School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, Malaysia.
Manuscript received on 16 February 2019 | Revised Manuscript received on 07 March 2019 | Manuscript Published on 08 June 2019 | PP: 665-671 | Volume-7 Issue-5S4, February 2019 | Retrieval Number: E11380275S419/19©BEIESP
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Abstract: Uncertainty analysis can be broadly classified into quantitative and qualitative types. An example of qualitative uncertainty is ‘words’ as a natural language in which can mean different things to different people. Hence, there is always exist an uncertainty in words or linguistic-linked assessment that need to be considered and manage wisely. Such uncertainty is commonly involve in decision-making problem as it highly dependent on human perceptions. This study explores the relationship between two variables namely the level of uncertainty to the input and the changes of output based on multi criteria decision support system. There is positive relationship between these two variables. Based on that, the novel technique of generating the interval type-2 fuzzy membership functions is proposed where it can accurately map the decision makers’ perceptions to the fuzzy set model which can reduce the potential of loss information. In literature, the output ranking of the system is presented as crisp number. However, this study proposed new form of output which is in interval form based on multi criteria decision support. Overall, this study provides a new insight of how we should not ignore the uncertainty when it affects the input by provide an intelligent way to map human perceptions to the system using fuzzy set.
Keywords: Fuzzy Set, Membership Functions, Multi Criteria Decision Support.
Scope of the Article: Information Retrieval