WHAT DO INDONESIAN NETIZENS THINK ABOUT THE EMONEY? : A SENTIMENT ANALYSIS WITH MACHINE LEARNING

• Published in: Jurnal riset akuntansi dan bisnis Airlangga Vol. 8; no. 1; pp. 1452 - 1469
• Main Authors: Timur, Yan Putra, Ratnasari, Ririn Tri, Hadi, T.Saipul, Sari, Dyah Permata
• Format: Journal Article
• Language: English
• Published: Universitas Airlangga 26.05.2023
• Subjects: e-money; machine learning; orange data mining; sentiment analysis; twitter
• ISSN: 2548-1401; 2548-4346
• DOI: 10.20473/jraba.v8i1.44940

This study aims to identify the most popular topics and words in conversations in cyberspace with the issue of E-Money. In addition, the research aims to find out how netizens feel about E-Money with the help of Machine Learning. This study uses a quantitative method with a sentiment analysis approach using the Machine Learning program, namely Orange Data Mining. The data used are tweets originating from Twitter that were crawled from April 5, 2023, to April 12, 2023. Researchers used the keywords E-Money," "Electronic Money," and "Electronic Money" to get a total of 800 tweets. The results showed that the words "Money," "Deposit," and "Tools" are the three words that appear most frequently in discussions of E-Money on Twitter, which is a registration procedure for using E-Money for the first time. In addition, E-Money is widely discussed in tweets in the form of Quizzes or giveaways, so in these tweets, E-money is used as a medium for transferring funds. Overall, the sentiment shown by netizens on Twitter is positive, with emotions dominated by feelings of joy and surprise towards E-Money. On the other hand, a tiny number still shows negatively, especially when experiencing technical problems when using E-Money, so concerns arise about the security of their money and personal data. Then the results of this study can be used by E-Money issuers as evaluation material to continue improving the security system so that E-Money users feel safe and satisfied and will continue to use E-Money for an extended period.