# Analysis and Ranking of IT Risk Factors Using Fuzzy TOPSIS-Based Approach
## 3 Methods and Results
### Fuzzy TOPSIS Method  
The fuzzy TOPSIS tactic is an approach that was developed from the TOPSIS core principle to address a wide range of MCDM challenges in an uncertain setting. [PERSON] and [PERSON] established the fuzzy TOPSIS procedure in 1992 by applying fuzzy values to the TOPSIS procedure [27]. [PERSON] introduced a vertex process to calculate the distance among two TFNs in 2000 [28]. TFNs would then portray the decision makers' perspectives on characteristics as well as alternatives in this strategy. The alternatives would then be ranked depending on the distance closest to ideal solutions, and the ranking consequence would be used to make the selection. The fuzzy TOPSIS process works on the same principles as the TOPSIS method, and yet in a more ambiguous setting. There are numerous benefits to using the fuzzy TOPSIS approach for solving MCDM challenges. To begin, the fuzzy TOPSIS procedure was introduced to resolve the ambiguity that frequently emerges in information derived from human decision. Let \(X_{1}=(x_{1},\,x_{2},\,x_{3})\) and \(Y_{1}=(y_{1},\,y_{2},\,y_{3})\) be two triangular fuzzy numbers (TFNs); therefore, the following Equation (1) could be used to calculate the distance between the two IFNs. \[d_{v}\Big{(}X_{1},\widetilde{Y}_{1}\Big{)}=\sqrt{\frac{1}{3}\Big{[}(x_{1}-y_{ 1})^{2}+(x_{2}-y_{2})^{2}+(x_{3}-y_{3})^{2}\Big{]}} \tag{1}\]  
Figure 3: Hierarchical structure for the evaluation. Furthermore, we have discovered numerous uncertain circumstances that have been predicted using TFNs to produce an improved result when solving MCDM major issues. Moreover, the fuzzy TOPSIS strategy is simple and straightforward for tackling MCDM challenges with imprecise information. According to a review of relevant literature [29; 30; 31; 32; 33], this process could be used as a separate strategy to solving MCDM challenges such as assessing various websites as well as examining the variables that assist to enhance the comparative benefit of those targeted online platforms. Figure 4 shows the sequential steps of the fuzzy TOPSIS method. In this research, six criteria and eight alternatives are consistently rated using the Fuzzy TOPSIS procedure. In classic multiple-criteria decision-making (MCDM) processes, the weights of characteristics illustrate correlative significance in the decision-making process. We cannot assume that each and every evaluation criterion is equally important because evaluating criteria includes a variety of perspectives and interpretations [29]. Subjective and objective strategies to weighing are the two types of weighing methods. Weight values are primarily generated using subjective strategies predicated on decision makers' choices or decisions. A decision matrix is a set of values in columns and rows that is used to clearly compare different solutions through weighing parameters according to their importance. Table 1 below summarizes the criterion form as well as weight allocated to every set of criteria. The type represents the category of different criteria. The researchers used the  
Figure 4: Steps of the fuzzy TOPSIS method. standard fuzzy scale (that can be seen in Table 2) as well as Equations (1)-(10) to collect and analyze the data. The strategies are evaluated using a variety of criteria, and the results of the decision matrix are demonstrated in the following table. The arithmetic mean of all 25 decision makers' opinions is provided in Table 3's preference matrix. The letters L, M, or U are used to demonstrate a triangular fuzzy number (TFN). The indicators L, M, as well as U, respectively, represent the least preferred, most preferred, and highest preferred significance. The fuzzy scale used in the model is shown in Table 2.  
The alternatives are assessed in aspects of different criteria, as well as the decision matrix consequences demonstrated below in Table 3. It should be noted that if more than one expert participates in the estimation, the matrix below actually reflects the arithmetic average of all specialists. (2) Step 2: Make a normalized decision matrix.