Sentiment Analysis of Indonesian Digital Payment Customer Satisfaction Towards GOPAY, DANA, and ShopeePay Using Naïve Bayes and K-Nearest Neighbour Methods

Authors

  • Anggita Putri Maharani Universitas Nasional, Jakarta
  • Agung Triayudi Universitas Nasional, Jakarta

DOI:

https://doi.org/10.30865/mib.v6i1.3545

Keywords:

Sentiment Analysis, Naïve Bayes, K-Nearest Neighbour, Machine Learning, Twitter

Abstract

The utilization of different results from the pace of technological development provides a lot of convenience, benefits, and time efficiency. Now, many related companies or organizations are engaged in technology, offering various services that they have developed to meet the needs of a highly consumptive society, with different terms and conditions. There are many descriptions related to the experience of using the platform that it developed on social media, where everyone can express their feelings and opinions about something since people commonly use Twitter. This study used sentiment analysis and opinion mining to see public satisfaction with digital payment services available in Indonesia by focusing on several available services such as GOPAY, DANA, and ShopeePay. The dataset that became the source of this research was taken from Twitter data through several preparation stages, including data crawling, data cleaning, feature selection, and classification with two machine learning approaches (K-Nearest Neighbour and Naïve Bayes). The raw data held is pre-processed until making the clean data. According to the classification algorithm, a feature search is implemented in this study so that the classification process and data modeling validation provide significant results.

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Published

2022-01-25

Issue

Vol. 6 No. 1 (2022): Januari 2022

Section

Articles

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