TY - GEN
T1 - Mining customers opinion on services and applications of mobile payment companies in Indonesia using sentiment analysis approach
AU - Prabaningtyas, Nadhila Idzni
AU - Surjandari, Isti
AU - Laoh, Enrico
N1 - Funding Information:
Authors would like to express gratitude and appreciation to Universitas Indonesia for funding this research through PIT-9 Research Grants Universitas Indonesia No: NKB-0061/UN2.R3.1/HKP.05.00/2019
Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - The development of technology and digital has also increased the ease of accessing the internet. One aspect of daily life that are affected by the adoption of technology and the internet is the field of payment transactions. Payment transactions are inseparable from everyday life. At this time with the development of technology, payment transactions can be done with the more practical, easy, safe and convenient. The technology is called Financial Technology. Mobile payment is a service that is part of financial technology. The aspects contained in the mobile payment are top up, transfers, cash withdrawals, online payment, and offline payments. Classifications of reviews from Twitter are classified using Support Vector Machine. The results of this study are Go-Pay and OVO must pay attention to every aspect and improve every aspect, of course, to increase customer satisfaction. The accuracy level of the classification model produced for bigram is 92% (Go-Pay) and 93% (OVO). It also shows that sentiment analysis using bigram can improve accuracy level.
AB - The development of technology and digital has also increased the ease of accessing the internet. One aspect of daily life that are affected by the adoption of technology and the internet is the field of payment transactions. Payment transactions are inseparable from everyday life. At this time with the development of technology, payment transactions can be done with the more practical, easy, safe and convenient. The technology is called Financial Technology. Mobile payment is a service that is part of financial technology. The aspects contained in the mobile payment are top up, transfers, cash withdrawals, online payment, and offline payments. Classifications of reviews from Twitter are classified using Support Vector Machine. The results of this study are Go-Pay and OVO must pay attention to every aspect and improve every aspect, of course, to increase customer satisfaction. The accuracy level of the classification model produced for bigram is 92% (Go-Pay) and 93% (OVO). It also shows that sentiment analysis using bigram can improve accuracy level.
KW - Mobile Payment
KW - N-Grams
KW - Sentiment Analysis
KW - Support Vector Machine (SVM)
KW - Text Mining
UR - http://www.scopus.com/inward/record.url?scp=85074883457&partnerID=8YFLogxK
U2 - 10.1109/ICSSSM.2019.8887643
DO - 10.1109/ICSSSM.2019.8887643
M3 - Conference contribution
AN - SCOPUS:85074883457
T3 - 2019 16th International Conference on Service Systems and Service Management, ICSSSM 2019
BT - 2019 16th International Conference on Service Systems and Service Management, ICSSSM 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th International Conference on Service Systems and Service Management, ICSSSM 2019
Y2 - 13 July 2019 through 15 July 2019
ER -