Comparison Analysis Of Social Influence Marketing For Mobile Payment Using Support Vector Machine
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Comparison Analysis Of Social Influence Marketing For Mobile Payment Using Support Vector Machine

Oman Fikriyan Prihono, Puspita Kencana Sari


There are many digital-based financial services today, one of them is mobile payment service. Users can deposit money and make online transaction with their smartphone through mobile application. Five mobile payment service providers with the most users in Indonesia, according to Dailysocial are GOPAY, OVO, LinkAja, DANA, and PayTren. This study uses sentiment analysis to classify user’s opinion into positive and negative classes. The classification method used is Support Vector Machine. This study utilizes three metrics, namely Net Sentiment, Share of Voice, and Social Influence Marketing Score. Those metrics are useful for knowing reputation, reach, and influence of brands in social media. The findings in this study indicate that GOPAY, OVO, DANA, and PayTren have a positive dominant sentiment, while LinkAja has a negative dominant sentiment. The brand with the biggest influence and reaches in the mobile payment industry is GOPAY. While the highest reputation brand is PayTren. The implication of this research is to encourage mobile payment providers to be able to monitor their brand conditions among their competitors by utilizing social network analysis method.


nformation Technology, Machine Learning, Big Data

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[1] A. M. K. Ferdiana and G. S. Darma, “Understanding Fintech Through Go – Pay,” vol. 4, no. 2, pp. 257–260, 2019.

[2] T. Lerner, Mobile Payment. Wiesbaden: Springer Vieweg, 2013.

[3] Bank Indonesia, “Informasi Perizinan Penyelenggara dan Pendukung Jasa Sistem Pembayaran,” Bank Indonesia, 2019. [Online]. Available: [Accessed: 20-Mar-2019].

[4] R. Eka, “Fintech Report 2018,” Jakarta, 2018.

[5] Hootsuite and W. A. Sosial, “DIGITAL 2019: INDONESIA,” 2019.

[6] W. A. Günther, M. H. R. Mehrizi, M. Huysman, and F. Feldberg, “Debating big data : A literature review on realizing value from big data,” J. Strateg. Inf. Syst., vol. 26, pp. 191–209, 2017.

[7] A. Amado, P. Cortez, P. Rita, and S. Moro, “Research trends on Big Data in Marketing : A text mining and topic modeling based literature analysis,” Eur. Res. Manag. Bus. Econ., vol. 24, no. 1, pp. 1–7, 2018.

[8] J. Ram, C. Zhang, and A. Koronios, “The implications of Big Data analytics on Business Intelligence : A qualitative study in China,” Procedia - Procedia Comput. Sci., vol. 87, pp. 221–226, 2016.

[9] N. Elgendy and A. Elragal, “Big Data Analytics in Support of the Decision Making Process,” Procedia - Procedia Comput. Sci., vol. 100, pp. 1071–1084, 2016.

[10] D. Blazquez and J. Domenech, “Big Data sources and methods for social and economic analyses,” Technol. Forecast. Soc. Chang., vol. 130, no. September 2017, pp. 99–113, 2018.

[11] S. Moro, P. Rita, and B. Vala, “Predicting social media performance metrics and evaluation of the impact on brand building : A data mining approach,” J. Bus. Res., p. 11, 2016.

[12] S. Singh, Social Media Marketing For Dummies. Indianapolis: Wiley Publishing, Inc., 2010.

[13] N. A. Vidya, M. I. Fanany, and I. Budi, “Twitter Sentiment to Analyze Net Brand Reputation of Mobile Phone Providers,” Procedia Comput. Sci., vol. 72, pp. 519–526, 2015.

[14] C. Ebster, C. Strauss, and F. Poecze, “Social media metrics and sentiment analysis to evaluate the effectiveness of social media posts,” Procedia Comput. Sci., vol. 130, pp. 660–666, 2018.

[15] M. Kubina, M. Varmus, and I. Kubinova, “Use of big data for competitive advantage of company,” vol. 26, no. 15, pp. 561–565, 2015.

[16] F. B. Bekoglu and C. Onaylı, “Strategic Approach in Social Media Marketing and a Study on Successful Facebook Cases,” Eur. Sci. Journal, ESJ, vol. 12, no. 7, p. 261, 2016.

[17] Y. Sung-Wook and C. H. Jeong, “Roles of Brand Reputation, Product Information and Discount Rate in Mobile Advertisement,” Korean J. Consum. Advert. Psychol., vol. 16, no. 2, pp. 291–308, 2017.

[18] A. Alamsyah, W. Rahmah, and H. Irawan, “SENTIMENT ANALYSIS BASED ON APPRAISAL THEORY FOR MARKETING INTELLIGENCE IN INDONESIA ’ S,” J. Theor. Appl. Inf. Technol., vol. 82, no. 2, pp. 335–340, 2015.

[19] P. Kotler and G. Amstrong, Principles of Marketing, Global. Harlow: Pearson Education Limited, 2016.

[20] N.-R. Lee and H. Dong-Hyun, “The Effects of Service Escape, Brand Reputation, Experiences on Brand Attitude and Loyalty in Korean Restaurants,” Culin. Soc. Korea, vol. 19, no. 3, pp. 173–193, 2013.

[21] I. H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Third. Burlington: Elsevier Inc., 2011.

[22] B. Liu, “Sentiment Analysis and Opinion Mining,” no. May, 2012.


[24] I. Muis and A. Muhammad, “Penerapan Metode Support Vector Machine (SVM) Menggunakan Kernel Radial Basis Function (RBF) Pada Klasifikasi Tweet,” J. Sains, Teknol. dan Ind., vol. 12, no. 2, pp. 189–197, 2015.

[25] M. Ahmad, “Analyzing the Performance of SVM for Polarity Detection with Different Datasets,” J. Mod. Educ. Comput. Sci., vol. 10, no. October, pp. 29–36, 2017.

[26] P. F. Kurnia and Suharjito, “Business Intelligence Model to Analyze Social Media Information,” Procedia Comput. Sci., vol. 135, pp. 5–14, 2018.

[27] Sugiyono, Metode Penelitian Kuantitatif. Bandung: Alfabeta, 2018.

[28] U. Sekaran and R. Bougie, Research Methods for Business: A Skill-Building Approach, Sixth. Chicherter, United Kingdom: John Wiley & Sons Ltd., 2013.

[29] Indrawati, Metode Penelitian Manajemen dan Bisnis Konvergensi Teknologi Komunikasi dan Informasi. Bandung: PT Refika Aditama, 2015.

[30] M. El Marrakchi, H. Bensaid, and M. Bellafkih, “Scoring Reputation in Online Social Networks,” 2015.

[31] V. A. Barger and L. I. Labrecque, “An Integrated Marketing Communications Perspective on Social Media Metrics,” J. Integr. Mark. Commun., pp. 64–76, 2013.

[32] P. K. Sari, A. Alamsyah, and S. Wibowo, “Measuring e-Commerce service quality from online customer review using sentiment analysis Measuring e-Commerce service quality from online customer review using sentiment analysis,” J. Phys., 2018.


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