TY - GEN
T1 - Customer Satisfaction Analysis of Mobile Banking Application Based on Twitter Data
AU - Ligiarta, Mahardhian Anjar
AU - Ruldeviyani, Yova
N1 - Funding Information:
This work is fully supported by Universitas Indonesia.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The Covid-19 pandemic has changed the way people transact from physical bank to mobile banking transactions. The banks as financial companies are competing to offer the best service for mobile banking. Failure to fulfill consumer needs can damage a bank's reputation, profitability, and lead to gradual loss of customers. Hence, the bank needs to know its performance by measuring customer satisfaction. This study is aimed to know customer satisfaction of mobile banking using sentiment analysis. Sentiment analysis was conducted using data from Twitter, knowing that Twitter is a widely used media social with text-based content. The collected data was then predicted using the Support Vector Machine (SVM) model to get a positive or negative sentiment. From the model training result, this model achieved 92.5% of accuracy. The research analyzes customer satisfaction using sentiment analysis of BCA Mobile, Livin' by Mandiri, BRI Mobile (Brimo), and BNI Mobile. Brimo received the greatest percentage of positive sentiment compared to other platforms. Livin' by Mandiri and Brimo had a serious issue regarding its reliability. BCA Mobile should be more concerned regarding its usefulness. Meanwhile, BNI Mobile should have been more worried about its platform responsiveness.
AB - The Covid-19 pandemic has changed the way people transact from physical bank to mobile banking transactions. The banks as financial companies are competing to offer the best service for mobile banking. Failure to fulfill consumer needs can damage a bank's reputation, profitability, and lead to gradual loss of customers. Hence, the bank needs to know its performance by measuring customer satisfaction. This study is aimed to know customer satisfaction of mobile banking using sentiment analysis. Sentiment analysis was conducted using data from Twitter, knowing that Twitter is a widely used media social with text-based content. The collected data was then predicted using the Support Vector Machine (SVM) model to get a positive or negative sentiment. From the model training result, this model achieved 92.5% of accuracy. The research analyzes customer satisfaction using sentiment analysis of BCA Mobile, Livin' by Mandiri, BRI Mobile (Brimo), and BNI Mobile. Brimo received the greatest percentage of positive sentiment compared to other platforms. Livin' by Mandiri and Brimo had a serious issue regarding its reliability. BCA Mobile should be more concerned regarding its usefulness. Meanwhile, BNI Mobile should have been more worried about its platform responsiveness.
KW - Customer satisfaction
KW - mobile banking
KW - sentiment analysis
KW - SVM
UR - http://www.scopus.com/inward/record.url?scp=85147333546&partnerID=8YFLogxK
U2 - 10.1109/ICE3IS56585.2022.10010221
DO - 10.1109/ICE3IS56585.2022.10010221
M3 - Conference contribution
AN - SCOPUS:85147333546
T3 - Proceedings - 2022 2nd International Conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2022
SP - 322
EP - 327
BT - Proceedings - 2022 2nd International Conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2022
A2 - Jussman, Yessi
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2022
Y2 - 4 November 2022 through 5 November 2022
ER -