Although the Delphi Technique has been widely used in education, especially in anticipation of the future, this method have its drawbacks. Among the weaknesses of the Delphi method (S. Siraj 2008), 1) Reliability of the data depends on expertise; if the researcher fails to deliver real experts mean the study will lose credibility, 2) Experiments are repeated on a sample and this will cause boredom to the sample, 3) A small number of experts are not able to resolve all the issues studied and 4) Less chance of getting a response from the emotional aspect.
To solve the problem of ambiguity in the consensus of experts, researchers from around the world have created new methods. Murray, T.J., Pipino, L. L & Gigch (1985) proposed the application of Fuzzy Delphi Method Theory into semantic variables used to solve the problem of ambiguity in the Delphi Technique. Fuzzy Delphi Method was derived to solve the problems of traditional Delphi Technique (Fuziah Rosman Mohd Nazri Ab Rahman, Saedah Siraj 2013; Glumac et al. 2011; Ishikawa, A., Amagasa, T., Tamizawa, G., Totsuta, R. and Mieno 1993; Saedah Siraj 2012).
The Fuzzy theory was introduced to improve time-consumption and solving the fuzziness of common understanding in experts’ opinions (Hwang, C.L. and Lin 1987; Noorderhaben 1995). The Ishikawa works used the maximum-minimum method together with cumulative frequency distribution and fuzzy scoring to compile the expert opinions into fuzzy numbers. The experts’ interval value was then used to derive the fuzzy numbers resulting in the FDM. This method was based on group thinking of the qualified experts that assures the validity of the collected information.
Hsu, H., & Chen (1996) proposed a fuzzy aggregate equation. By using this similarity function, the similarity between experts can be collected and fuzzy numbers can be built directly into each expert to determine the degree of agreement between them. Then the coefficient of consensus is used to get value assessment fuzzy numbers for all specialists. If the degree of agreement is too low among experts, then the questionnaire must be administered again. The advantages of Fuzzy Delphi Method are; 1) saves time on the questionnaire, 2) save costs, 3) reduce the total number of surveys, questionnaires increase the recovery rate, 4) experts can fully express their opinions, ensure completeness and consistency of opinion and 5) taking into account the ambiguity that cannot be avoided during the study. This method does not misinterpreted original expert opinion and gives their real reactions.
Syamsul Nor Azlan Mohamad., Mohamed Amin Embi. & Norazah Nordin. 2015. Determining e-Portfolio Elements in Learning Process Using Fuzzy Delphi Analysis. International Education Studies, 8(9), 171–176. doi:10.5539/ies.v8n9p171 IES 46597-181426-1-PB
Fuzzy Delphi Analysis v2.0 (Engine Calculation).
In addition, I take an initiative to upgrade and improvise the calculation engine of fuzzy Delphi created by (Mohd Ridhuan Mohd Jamil, Saedah Siraj, Zaharah Hussin 2014) with the latest version of Fuzzy Delphi Analysis Software v2.0. The improvisation are; 1) re-identify and re-calculate the fuzzy sets and numbers, 2) automate and synchronize the Fuzzy Delphi variable with a likert scales and 3) automate defuzzification and ranking-based system. Click here to download the software, password : 123456, Fuzzy Delphi Analysis v2.0)
Table Variable for the importance weight of criteria.
|Strongly disagree||(0.0, 0.1, 0.2)|
|Disagree||(0.1, 0.2, 0.4)|
|Not Sure||(0.2, 0.4, 0.6)|
|Agree||(0.4, 0.6, 0.8)|
|Strongly Agree||(0.6, 0.8, 1.0)|
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