Assessment of mobile payment service based on user review in Indonesia
Article Sidebar
Main Article Content
Abstract
This study evaluates user satisfaction with mobile payment services, focusing on sentiment of user reviews from Twitter. Four key dimensions—reliability, economic benefits, assurance, and responsiveness—were analyzed for two applications, DANA and LinkAja. This study used Support Vector Machine algorithm with an accuracy measurement using the Confusion Matrix reaching 83.83% for DANA and 82,58% for LinkAja. The ROC curve showed the best AUC result of 0.909 for DANA and 0.900 for LinkAja (Excellent Classification). Sentiment analysis revealed that both applications faced predominantly negative sentiment, except for the economic benefit dimension of LinkAja, which showed a higher proportion of positive sentiment. Major issues identified include slow problem resolution, unresponsive customer service, and occasional application errors. These challenges highlight user dissatisfaction and the need for improved customer service and system reliability. The findings underscore the importance of addressing user complaints promptly to enhance satisfaction and foster loyalty.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Allahyari, M., Pouriyeh, S., Assefi, M., Safaei, S., Trippe, E. D., Gutierrez, J. B., & Kochut, K. (2017). A brief survey of text mining: Classification, clustering and extraction techniques. ArXiv Preprint ArXiv:1707.02919.
Amoroso, D. L., & Ackaradejruangsri, P. G. (2018). The Mobile Wallet Explosion in Thailand : Factors Towards Predicting Consumer Loyalty. Asia Pacific Journal of Information Systems, 28(4), 290–307. https://doi.org/10.14329/apjis.2018.28.4.290
Azizah, N., Handayani, P. W., & Azzahro, F. (2018). Factors Influencing Continuance Usage of Mobile Wallets in Indonesia. Proceedings of 2018 International Conference on Information Management and Technology, ICIMTech 2018, September, 92–97. https://doi.org/10.1109/ICIMTech.2018.8528157
Bagla, R., & Sancheti, V. (2018). Gaps in customer satisfaction with digital wallets: challenge for sustainability. Journal of Management Development, 37. https://doi.org/10.1108/JMD-04-2017-0144
Bose, A., & Mellado, B. (2018). World Payments Report 2018. 55.
Bramer, M. (2007). Principles of data mining. In Undergraduate Topics in Computer Science. Springer.
Chandra, S., Srivastava, S. C., & Theng, Y.-L. (2010). Evaluating the role of trust in consumer adoption of mobile payment systems: An empirical analysis. Communications of the Association for Information Systems, 27(1), 29.
Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 1–27.
Chawla, D., & Joshi, H. (2019). Consumer attitude and intention to adopt mobile wallet in India – An empirical study. International Journal of Bank Marketing, 37(7), 1590–1618. https://doi.org/10.1108/IJBM-09-2018-0256
Devita, V. D. (2019). Siapa Aplikasi E-wallet dengan Pengguna Terbanyak di Indonesia? Https://Iprice.Co.Id/Trend/Insights/e-Wallet-Terbaik-Di-Indonesia/.
Genc-Nayebi, N., & Abran, A. (2017). A systematic literature review: Opinion mining studies from mobile app store user reviews. Journal of Systems and Software, 125, 207–219.
Goodman, J. (2019). Strategic customer service: Managing the customer experience to increase positive word of mouth, build loyalty, and maximize profits. In Amacom.
Gorunescu, F. (2011). Data Mining: Concepts, models and techniques (Vol. 12). Springer Science & Business Media.
Herman, & Mononimbar, D. A. (2017). Indonesia Fifth-Largest Country in Terms of Twitter Users. Jakarta Globe. Http://Jakartaglobe.Id/News/Indonesia-Fifth-Largest-Country-in-Terms-Twitter-Users.
Hyun Soon, J., You, Y. Y., & Jeon, J. S. (2019). The effect of mobile payment service quality on usage intention - Focusing on the adjustment effect of user satisfaction. International Journal of Innovative Technology and Exploring Engineering, 8(3), 202–204.
Indrawati, & Putri, D. A. (2018). Analyzing factors influencing continuance intention of E-payment adoption using modified UTAUT 2 Model: (A case study of Go-Pay from Indonesia). 2018 6th International Conference on Information and Communication Technology, ICoICT 2018, May, 167–173. https://doi.org/10.1109/ICoICT.2018.8528748
Ismagilova, E., Dwivedi, Y. K., Slade, E., & Williams, M. D. (2017). Electronic Word of Mouth (eWOM) in the Marketing Context. https://doi.org/https://doi.org/10.1007/978-3-319-52459-7
JAKPAT. (2020). Indonesia Digital Wallet Trend 2019, JAKPAT Special Report - 2nd Semester of 2019.
Juneja, D., Ahmad, S., & Kumar, S. (2011). Adaptability of total quality management to service sector. International Journal of Computer Science & Management Studies, 11(2), 93–98.
Kar, A. K. (2021). What affects usage satisfaction in mobile payments? Modelling user generated content to develop the “digital service usage satisfaction model.” Information Systems Frontiers, 23(5), 1341–1361.
Kristiyanti, D. A., Putri, D. A., Indrayuni, E., Nurhadi, A., & Umam, A. H. (2020). E-Wallet Sentiment Analysis Using Naïve Bayes and Support Vector Machine Algorithm. Journal of Physics: Conference Series, 1641(1), 012079.
Maxham, J. G., & Netemeyer, R. G. (2003). Firms reap what they sow: The effects of shared values and perceived organizational justice on customers’ evaluations of complaint handling. Journal of Marketing, 67(1), 46–62. https://doi.org/10.1509/jmkg.67.1.46.18591
Miner, G. D., Elder, J., & Nisbet, R. (2012). Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications. https://doi.org/10.1016/C2010-0-66188-8
Mustaqim, K., Amaresti, F. A., & Dewi, I. N. (2024). Analisis Sentimen Ulasan Aplikasi PosPay untuk Meningkatkan Kepuasan Pengguna dengan Metode K-Nearest Neighbor (KNN). Edumatic: Jurnal Pendidikan Informatika, 8(1), 11–20.
Ningri, L. J., Hamidi, M., & Adrianto, F. (2023). Sentiment Analysis Against Digital Payment “GoPay”,“OVO”,“DANA”, and “ShopeePay” Using Naïve Bayes Classifier Algorithm. Indonesian Journal of Economics and Management, 3(2), 322–336.
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). Servqual: A multiple-item scale for measuring consumer perc. Journal of Retailing, 64(1), 12.
Parasuraman, A., Zeithaml, V. A., & Malhotra, A. (2005). ES-QUAL: A multiple-item scale for assessing electronic service quality. Journal of Service Research, 7(3), 213–233.
Park, J., Ahn, J., Thavisay, T., & Ren, T. (2019). Examining the role of anxiety and social influence in multi-benefits of mobile payment service. Journal of Retailing and Consumer Services, 47, 140–149.
Park, J. K., Ahn, J., Thavisay, T., & Ren, T. (2019). Examining the role of anxiety and social influence in multi-benefits of mobile payment service. Journal of Retailing and Consumer Services, 47(April 2018), 140–149. https://doi.org/10.1016/j.jretconser.2018.11.015
Prabaningtyas, N. I., Surjandari, I., & Laoh, E. (2019). Mining customers opinion on services and applications of mobile payment companies in Indonesia using sentiment analysis approach. 2019 16th International Conference on Service Systems and Service Management (ICSSSM), 1–5.
Putri, D. A., Kristiyanti, D. A., Indrayuni, E., Nurhadi, A., & Hadinata, D. R. (2020). Comparison of naive bayes algorithm and support vector machine using pso feature selection for sentiment analysis on e-wallet review. Journal of Physics: Conference Series, 1641(1), 012085.
Rodrigues, P., Silva, I. S., Barbosa, G. A. R., Coutinho, F. R. dos S., & Mourão, F. (2017). Beyond the stars: Towards a novel sentiment rating to evaluate applications in web stores of mobile apps. Proceedings of the 26th International Conference on World Wide Web Companion, 109–117.
Safi’I, I., Putra, Y. P., & Vitasmoro, P. (2019). Priority for improvement of mobile banking services using Kano model and QFD. AIP Conference Proceedings, 2114(May), 1–7. https://doi.org/10.1063/1.5112473
Salim, A., Setiawan, M., Rofiaty, R., & Rohman, F. (2018). Focusing on complaints handling for customer satisfaction and loyalty: The case of Indonesian public banking. European Research Studies Journal, 21(3), 404–416. https://doi.org/10.35808/ersj/1071
Saputra, S. A., Rosiyadi, D., Gata, W., & Husain, S. M. (2019). Analisis sentimen E-Wallet pada google play menggunakan algoritma naive bayes berbasis particle swarm optimization. Jurnal Resti, 3, 377–382.
Septiani, K. (2022). Perbandingan Analisis Sentimen Terhadap Pembayaran Digital “Go-Pay” Dan “Ovo” Di Media Sosial Twitter Menggunakan Metode Naive Bayes Dan Word Cloud. Jurnal Ilmu Data, 2(10).
Sianipar, A. N., & Devega, M. (2024). Analisis Sentimen Ulasan Pengguna Aplikasi Dana dan Linkaja Menggunakan Metode Naïve Bayes. Zonasi: Jurnal Sistem Informasi, 6(2), 510–522.
Singh, N., Sinha, N., & Liébana-Cabanillas, F. J. (2020). Determining factors in the adoption and recommendation of mobile wallet services in India: Analysis of the effect of innovativeness, stress to use and social influence. International Journal of Information Management, 50(May 2019), 191–205. https://doi.org/10.1016/j.ijinfomgt.2019.05.022
Timones. (2019). Total e-wallet size in Indonesia likely to hit $15 billion by 2020 2019. Available from: Http://Www.Theasianbanker.Com/Updatesand- Articles/Indonesia-Mobile-Payments-Still-Closely-Tied-to-Mobiletop- Ups-and-Online-Purchases.
Wang, L., Luo, X. (Robert), Yang, X., & Qiao, Z. (2019). Easy Come or Easy Go? Empirical Evidence on Switching Behaviors in Mobile Payment Applications. Information & Management, 56(7). https://doi.org/10.1016/j.im.2019.02.005
Wu, X. (2009). The top ten algorithms in data mining. CRC Press Google Schola, 2, 133–143.
Zahra, S., & Alamsyah, A. (2022). Digital Wallet Service Quality Analysis using Multiclass Classification and Sentiment Analysis. 2022 5th International Conference on Information and Communications Technology (ICOIACT), 256–261.
Zeithaml, V. A. (2000). A conceptual framework for understanding e-service quality: Implications for future research and managerial practice. MSI Working Paper Series: 1.
Zuo, W., Zhu, W., Chen, S., & He, X. (2019). Service quality management of online car-hailing based on PCN in the sharing economy. Electronic Commerce Research and Applications, January. https://doi.org/10.1016/j.elerap.2019.100827