Page 28 - POSTER FYP MAC-OGOS 2025

AN INTEGRATED FUZZY DEMATEL AND FUZZY TOPSIS METHOD FOR ANALYZING

STUDENTS’ MOBILE BANKING PROVIDER PREFERENCES

ANATASHA NABILAH BINTI SUHAIMI SUPERVISOR:

2023627432 K242/29 NUR ELINI BINTI JAUHARI

ABSTRACT

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Mobile banking is widely used by university students, creating a need to understand what influences their choice of banking platforms. This study evaluates student

preferences using an integrated Fuzzy DEMATEL and Fuzzy TOPSIS approach. The research involves three main phases. The first phase was identifying the evaluation

criteria namely accessibility, features and functionality, security, transaction speed, and user-friendly. Second phase was calculating criteria weights using Fuzzy

DEMATEL and third phase was ranking mobile banking providers using Fuzzy TOPSIS. The results show that accessibility is the most important factor, followed by user-

friendly and features and functionality. Among the three alternatives, Maybank was ranked as the most preferred provider among UiTM Machang students. These

findings provide useful insights for banks to improve mobile banking services based on student needs.
PROBLEM STATEMENT IMPLEMENTATION

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Mobile banking has become essential for
PHASE 2: FUZZY DEMATEL
daily financial activities, especially among PHASE 2: FUZZY DEMATEL Step 8: Generate the standardized Step 9: Compute the total
relation matrix
direct influence matrix
university students. Although banks offer Step 2: Construct the Fuzzy Direct-Relation Matrix
advanced features, the specific needs and
satisfaction of students are often
Step 10: Calculate the center and Step 11: Calculate the relative
overlooked. Most studies focus on general cause degrees weight

users, with limited research targeting Step 3: Normalize the Fuzzy Direct-Relation Matrix

university students. Additionally, Fuzzy

DEMATEL and Fuzzy TOPSIS have often been

used independently, with limited integration

in mobile banking evaluation. Therefore, Step 4: Calculate Left and Right Normalized Score PHASE 3: FUZZY TOPSIS
PHASE 3: FUZZY TOPSIS

there is a need to analyze student

preferences using an integrated approach to Step 2: Construct the fuzzy Step 3: Normalize the fuzzy decision matrix

decision matrix

better understand service quality and

satisfaction. Step 5: Obtain the crips values

OBJECTIVES
O B J E C T I V E S Step 4: Calculate the weighted normalized decision matrix

To calculate the relative weights of

criteria using the Fuzzy DEMATEL Step 6: Rescaled the crips values

Step 5: Identify the FPIS and FNIS Step 7: Compute the CCi and rank

method

To rank the students’ preference of

Step 7: Obtain the direct-relation matrix
mobile banking provider using the Fuzzy

Step 6: Calculate the distance of FPIS and FNIS

TOPSIS method

METHODOLOGY

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RESULTS & functionality are the cause criteria, while accessibility and user-friendly are effect
PHASE 1 The Fuzzy DEMATEL analysis showed that transaction speed, security, and features &
PHASE 1

Criteria selection R E S U L T S &

Start Alternative selection criteria. Accessibility received the highest weight, showing students care more about ease

DISCUSSION
D I S C U S S I O N of access. Fuzzy TOPSIS results ranked Maybank as the most preferred mobile banking
Data collection

provider, followed by RHB, and Bank Islam. These findings highlight the need for banks to

improve in areas like transaction speed and user experience, as improvements in driving

criteria can positively impact overall student satisfaction.

PHASE 2: FUZZY DEMATEL PHASE 3: FUZZY TOPSIS
PHASE 2: FUZZY DEMATEL
PHASE 3: FUZZY TOPSIS

Construct the fuzzy direct- Construct the fuzzy direct RECOMMENDATIONS
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relation matrices matrix CONCLUSION R E C O M M E N D A T I O N S
Normalize the fuzzy direct- Normalize the fuzzy decision
relation matrices This study used Fuzzy DEMATEL and
matrix Expand the sample group such as

Calculate the left and right Calculate the weighted Fuzzy TOPSIS to analyze students' participants from different age groups,

normalized score
normalized decision matrix preferences for mobile banking providers. backgrounds, and locations.

Obtain the crip values The results showed that accessibility was
Calculate FPIS and FNIS Conduct comparative studies between

Obtain the direct-relation Compute the closeness the most important factor, followed by students, working adults, and senior

matrix

coefficients user-friendliness, features, security, and citizens.

Standardized the direct transaction speed. Maybank was ranked

influence matrix Rank the alternatives Use other MCDM methods such as Fuzzy

as the most preferred provider, followed

Compute the total relation AHP, Fuzzy ANP, or Fuzzy VIKOR to

matrix by RHB and Bank Islam. The combination validate or enhance the findings.

Calculate the center and cause of both methods helped to clearly identify Apply in other fields such as e-wallets,

degrees which factors matter most to students

End e-commerce, or online education

Calculate the relative weights and which provider performs best based

of each criterion platforms.

on these factors.