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P-ISSN: 2715-2448 | E-ISSN: 2715-7199 
Vol.6 No.2 July 2025 
Buana Information Technology and Computer Sciences (BIT and CS) 

Decision Support System for the Most Chosen and Preferred 

Smartphone Using the MOORA Method 
 

Rianna Muhammad Rizqy Syahal Maulana1, Ryan hidayat2 
1,2 Faculty of Technology Law and Business, Sugeng Hartono University, Sukoharjo, Indonesia 

E-mail: syahalmaulana1919@gmail.com1, hryan3480@gmail.com2 

 
Received: 2025/01/06 | Revised: 2025/07/04 | Accepted: 2025/07/30 

 

 

Abstract  

 

The rapid development of the digital era in Indonesia has posed difficulties for consumers in choosing 

the right smartphone for them. This research aims to develop a DSS (Decision Support System) using 

the MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) method to determine the 

best smartphone according to consumer preferences, with criteria including smartphone price, design, 

usability flexibility, durability, performance, and camera quality. Evaluation is carried out by 

calculating the weight value of each criterion and ranking the smartphone alternatives based on a 

questionnaire. The research results show that the MOORA method is able to determine the best 

smartphone according to the people of Solo and help consumers choose the right smartphone for them. 

This research involves five of the most widely used smartphones in Indonesia (Apple, Samsung, Xiaomi, 

Oppo, Vivo, and Infinix) using the MOORA (Multi-Objective Optimization on the Basis of Ratio 

Analysis) method based on the defined criteria. The results show that Xiaomi smartphones rank first 

both in terms of quality and user quantity. The practical implications of this research are significant, 

providing consumers with a data-driven approach to make informed smartphone choices, thereby 

enhancing their purchasing satisfaction. 

 

Keywords: Smartphone, DSS, MOORA, Preference 

 

I. Introduction 

Smartphones have become an essential part of everyday life, not only as communication tools but 

also as multifunctional devices encompassing entertainment, work, and education. In Indonesia, the 

smartphone market continues to grow rapidly, driven by the growth of the younger generation and the 

increasing use of technology. Smartphones themselves are electronic devices that function like mobile 

phones but are equipped with additional capabilities such as running applications, internet access, 

multimedia players, and various other features typically found on computers. Smartphones generally 

use operating systems such as Android or iOS, which allow users to install additional applications as 

needed. According to the Kamus Besar Bahasa Indonesia (KBBI), a smartphone is "a smart phone, a 

mobile phone that has various computer functions, such as accessing the internet, receiving and sending 

emails, and so on." 

In 2024, it is estimated that the number of smartphone users in Indonesia will reach 194.26 million 

people. This number has increased by 2.23% compared to 2023, which had 190.03 million users [1]. 

However, as the smartphone market in Indonesia grows, consumers find it difficult to determine the 

right smartphone for them. The difficulty in determining a suitable smartphone necessitates the creation 

of a Decision Support System (DSS) to assist consumers in selecting their smartphones based on the 

feedback provided by consumers this year through questionnaires distributed to the citizens of Solo 

city. 

Recent advancements in smartphone technology, such as the integration of AI and improved 

camera systems, have significantly influenced consumer preferences. Understanding these trends is 

crucial for developing an effective DSS that aligns with the evolving market dynamics. This study aims 

to create a DSS that can analyze and recommend smartphones to consumers using the MOORA method. 

mailto:syahalmaulana1919@gmail.com
mailto:hryan3480@gmail.com


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It is hoped that this research will provide consumers with a clear picture of the current favorite 

smartphones, along with clear and easily understandable data-driven analysis. 

 
II. Method 

1. Decision Support System (DSS)   

According to experts, a Decision Support System (DSS) is an interactive system that supports the 

decision-making process by using a combination of data, models, and user interfaces (UI) to evaluate 

various decision scenarios. DSS is designed to address problems with unique characteristics, requiring 

a flexible, interactive system that can be tailored to the user's needs. DSS is typically used by middle to 

upper-level managers to aid in making strategic and tactical decisions involving many variables and 

uncertainties. [2] 

The main benefit of DSS is to speed up the decision-making process. DSS allows decision-makers 

to obtain accurate and up-to-date information, enabling them to make better and faster decisions. DSS 

also helps reduce subjectivity in decision-making by providing objective and verifiable data. 

 

2. Research methodology 

The research method is a scientific way to obtain valid data, with the aim of finding, developing, 

and proving certain knowledge so that it can be used to understand, solve, and anticipate problems [4]. 

This study employs a quantitative approach. The data collection technique uses Google Forms by 

distributing them to individuals aged 18-30 in the Solo area. 

The questionnaire was distributed to 104 respondents with 6 alternatives and 6 criteria. The data 

collection tool was developed with a closed questionnaire, namely a set of lists of statements or 

questions with possible answers that have been provided, so respondents only choose one of five 

alternative answers [5].  

 

3. MOORA Methods  

The Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) method was first 

introduced by Brauers and Zavadkas [6]. MOORA is a multi-objective system developed to optimize 

multiple conflicting attributes at the same time [7],[9]. 

One of the advantages of the MOORA method is its ability to address objectives for conflicting 

criteria, where criteria can either be beneficial (benefit) or non-beneficial (cost) [10]. This method 

generates a final score for each option, which is then ranked based on the alternatives with the highest 

value. The steps involved in completing this procedure are as follows [11],[12]: 

 

a. Preparing the Decision Matrix 

 

(1) 

      

b. Calculating the Normalization Matrix 

 

(2) 

      

c. Calculating the Preference Values 

In this step, which is the core of the process, each attribute is multiplied by the criteria weights 

for each alternative, then the results of the advantage criteria are added and subtracted from the 

results of the disadvantage criteria using the following formula [14],[15],[13]:  

 

 



 

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(3) 

 

III. Results and Discussion 

1. Application of Alternatives 

In determining smartphone recommendations to help the community in choosing the right 

smartphone using the MOORA method, it starts with determining the alternative samples used. 

 

Table 1. Alternative Data  

 

 

 

 

 

 

2. Application of Criteria 

The following is the assessment criteria data from the Decision Support System in Determining 

the Most Favorite Smartphone Using the MOORA Method 

Table 2. Criteria Description 

 

 

 

 

 
 

 

3. Alternative Weights and Criteria 

The following table 3 contains data on the alternative values of each criterion.  
 

Table 3. Alternative and Criteria 

Alternatif C1 C2 C3 C4 C5 C6 

A1 2 4 4 4 3 4 

A2 3 4 4 3 3 5 

A3 4 4 4 4 4 4 

A4 4 3 3 4 3 4 

A5 3 4 3 2 4 4 

A6 4 4 3 4 4 4 

 
4. Decision Matrix 

The data in Table 3 is converted into a matrix consisting of columns and rows to facilitate 

calculations in the next step, as presented below. 
 

       

Alternative Brand 

A1 Apple 

A2 Samsung 

A3 Xiaomi 

A4 Oppo 

A5 Vivo 

A6 Invinix 

Criteria Description 
Weight Value 

(Wj) 
Type 

C1 Price 2 Cost 

C2 Flexible 1 Benefit 

C3 Design 1,5 Benefit 

C4 Performance 3,0 Benefit 

C5 Durable 1,5 Benefit 

C6 Camera 1 Benefit 



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𝑋 =

[
 
 
 
 
 
2 4 4 4 3 4
3 4 4 3 3 5
4 4 4 4 4 4
4 3 3 4 3 4
3 4 3 2 4 4
4 4 3 4 4 4]

 
 
 
 
 

                                                         (4) 

         
5. Matrix Normalization 

Normalization aims to unite each matrix element so that the matrix elements have uniform values 

[16],[17]. Here is the matrix normalization based on data in the decision matrix using equation 3 above: 
 

𝑋𝑖𝑗 =

[
 
 
 
 
 
0,2390 0,4240 0,4618 0,4558 0,3464 0,3903
0,3585 0,4240 0,4618 0,3419 0,3464 0,4879
0,4781 0,4240 0,4618 0,4558 0,4618 0,3903
0,4781 0,3180 0,3464 0,4558 0,3464 0,3903
0,3585 0,4240 0,3464 0,2279 0,4618 0,3903
0,4781 0,4240 0,3464 0,4558 0,4618 0,3903]

 
 
 
 
 

                          (5) 

      

6. Optimizing Attribute 

The optimization value for each alternative is determined by summing the product of the criteria 

weights and the maximum attribute values (benefit type) and subtracting the sum of the product of the 

criteria weights and the minimum attribute values (cost type). [18]-[20]. 
 

𝑋𝑖𝑗 =

[
 
 
 
 
 
0,2390(2) 0,4240(1) 0,4618(1,5) 0,4558(3) 0,3464(1,5) 0,3903(1)

0,3585(2) 0,4240(1) 0,4618(1,5) 0,3419(3) 0,3464(1,5) 0,4879(1)

0,4781(2) 0,4240(1) 0,4618(1,5) 0,4558(3) 0,4618(1,5) 0,3903(1)
0,4781(2) 0,3180(1) 0,3464(1,5) 0,4558(3) 0,3464(1,5) 0,3903(1)

0,3585(2) 0,4240(1) 0,3464(1,5) 0,2279(3) 0,4618(1,5) 0,3903(1)

0,4781(2) 0,4240(1) 0,3464(1,5) 0,4558(3) 0,4618(1,5) 0,3903(1)]
 
 
 
 
 

             (6) 

 

Weighted Matrix Normalization Results: 

𝑋𝑖𝑗 =

[
 
 
 
 
 
0,4730 0,4240 0,6927 1,3674 0,5196 0,3903
0,7170 0,4240 0,6927 1,0257 0,5196 0,4879
0,9560 0,4240 0,6927 1,3674 0,6927 0,3903
0,9560 0,3180 0,5196 1,3674 0,5196 0,3903
0,7170 0,4240 0,5196 0,6837 0,6927 0,3903
0,9560 0,4240 0,5196 1,3674 0,6927 0,3903]

 
 
 
 
 

                                      (7) 

 
     

 

7. Rangking Y Value 

Calculating the preference value for each alternative (student) using the MOORA formula, 

involves data normalization, Determining the criterion weight, changing the criterion value into a 

matrix, and the preferences of each criterion [21]. The last stage in the DSS process using the MOORA 

Method is to determine the ranking [22].  
 

Tabel 4. Nilai Yi 

Alternatif Max Min Yi Ranking 

A1 3,867 0,424 3,443 4 

A2 3.866 0,424 3,442 5 

A3 4,523 0,424 4,099 1 

A4 4,070 0,318 3,752 3 

A5 3,427 0,424 3,003 6 

A6 4,350 0,424 3,926 2 

 



 

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The last stage in the DSS process using the MOORA Method is to determine the ranking [24].  

 

Tabel 5. Rankingan Alternatif 

Merk Mobil Ranking 

Xiaomi 1 

Infinix 2 

Oppo 3 

Apple 4 

Samsung 5 

Vivo 6 

 
Based on the calculation results using the MOORA method related to the smartphone brand, the 

alternative with code A1, the Xiaomi handphone brand, is ranked 1st with a value of 4,099. 

 
IV. Conclusion 

This study has succeeded in developing a Decision Support System (DSS) with the MOORA method to 

identify the most favorite smartphone brands based on the preferences of smartphone users. The MOORA method 

excels in processing multiple criteria, simplifying data normalization, and producing objective final scores. 

 
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