**1. Introduction**

Decision making is a problem solving process that produces a goal of factors such as subjectivity and linguistics which tend to be presented in real life to a lower or greater level [1]. Difficulties are often encountered when a problem involves several alternatives and the factors that influence it (criteria), to overcome this problem, it is able to use the Multi-Attribute Decison Making (MADM) method. The results of these methods still contain uncertainty so that in this case fuzzy logic plays an important role in overcoming problems that contain uncertainty. Fuzzy logic is the basis of a system that can implement a problem and solve sharp

problems [2]. However, Fuzzy MADM is only able to solve the problem of uncertainty in the data presented and numbers of diverse attributes is usually conflicting, thus to make a decision there needs to be a classic MADM method, so that decisions are more precise and more accurate [3], besides this method can also be used to provide input to the doctor so that there is no mistake in diagnosing dengue disease. One of the classic MADM methods that can be used is Simple Additive Weighting.

Simple Additive Weighting is often referred as a method with weighted sum. The basic concept of SAW method is to find a weighted sum of performance branches on each alternative of all attributes [4]. One of the problems that can be solved by this method is the misdiagnosis of DHF. DHF is a type of infectious disease caused by the degue virus which is transmitted through the bite of the *Aedes aegypti* and *Aedes albopictus* mosquitoes. DHF is often misdiagnosed with Typoid Fever, Morbili, ARI, Ensafalitis and Acute Pharyngitis. These errors occur because the initial symptoms that arise from the five diseases are almost the same as DHF [5]. However, in this case the application of SAW method is less effective if a Decision Support System is made so that a development method is needed. The development method that can be used is the FMADM method with its development or often called Fuzzy Decision Making (FDM). This method is development method of the classic MADM method. The results of SAW method will be used as a level of importance or input on the FDM method. The combination of these two methods will produce more optimal output.

### **2. Methodology and realization**

#### **2.1 Designing FMADM with SAW and FDM**

The data used are primary and secondary data, primary data obtained from the results of doctor interviews and secondary data is data on patients with DHF, secondary data will be used to validate the system. Completion of cases of dengue diagnosis will be through SAW method then the results of SAW method are used in the FDM method.

The first method will use one crisp value with 1 degree membership and use preference weight multiplication while the second method uses 3 crisp values namely right boundary, left boundary and crisp value with 1 membership degree which will later go through the aggregation process and total integral value.

#### **2.2 The FMADM method with SAW to diagnose a type of disease**

Completion using the FMADM method with SAW:

#### *2.2.1 Determine alternative sets and criteria*

Alternative (Ai) is a1 = Morbili, a2 = DBD, a3 = ARI, a4 = Typoid fever, a5 = Acute pharyngitis, a6 = Ensafalitis. Ci criteria are c1 = Fever, c2 = Spots, c3 = Bleeding gum, c4 = Nausea, c5 = Headache, c6 = Defecation Disorders

#### *2.2.2 Determine the criteria weight*

The weight of the criteria is obtained from triangular fuzzy numbers which are then converted into the form of crisp.

*Fuzzy Multi-Attribute Decision Making (FMADM) Application on Decision Support… DOI: http://dx.doi.org/10.5772/intechopen.94614*

#### *2.2.2.1 Fever*

The author defines the universal value for the criteria for fever is [0,1] and divides it into 5 categories of fuzzy triangle sets, which are normal (N), low fever (DR), moderate fever (DS), high fever (DT), very high fever (DST).

By using the concept of the Likert scale and the defuzzy method, Large of Maximum, **Table 1** is obtained as the weight of the criteria for fever.

### *2.2.2.2 Spots (Petheciae)*

The author defines the universal value for the criteria of spots is [0,1] and divides them into 5 categories of fuzzy triangle sets which are none (TA), few (SDK), somewhat a lot (ABYK) many (BYK), very much (SBYK). By using the concept of the Likert scale and the defuzzy method, Large of Maximum, the **Table 2** is obtained as the weight of the criteria for spots:

#### *2.2.2.3 Bleeding gum*

We are defines the universal value for bleeding gum criteria is [0,1] and divides it into 2 fuzzy triangle set categories namely never (TP), ever (P). By using the concept of the Likert scale and the defuzzy method, Large Of Maximum, the **Table 3** is obtained as the weight of the bleeding gum criteria.

#### *2.2.2.4 Nausea*

The author defines the universe value for the nausea criteria is [0.1] and divides it into 4 fuzzy triangle set categories namely never (TP), ever (P), rare (J) and often (S). By using the concept of the Likert scale and the defuzzy method, Large of Maximum, **Table 4** is obtained as the weight of the criteria for nausea.


### **Table 1.**

*Weight of fever.*


**Table 2.** *Weigth of spots.*


**Table 3.**

*Weight of bleeding gum.*


**Table 4.** *Weigth of nausea.*
