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

Abstract

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.

Keywords

nformation Technology, Machine Learning, Big Data

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References

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