Analisis Sentimen Masyarakat Indonesia Terhadap Dampak Penurunan Global Sebagai Akibat Resesi di Twitter
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Stephen Aprius Sutresno
*
Universitas Katolik Indonesia Atma Jaya, Jakarta,
Indonesia
(*) Corresponding Author
Abstract
A recession is a significant reduction in economic activity and is spread across the economy at its greatest for more than a few months, but it can also be seen in Real GDP, Real Income, Employment, Industrial Production, and Wholesale-Retail Sales. Recently, there has been a lot of public opinion regarding the recession that will occur in 2023, especially in Indonesia, on various social media such as Twitter. Based on these problems, sentiment analysis was carried out on tweets to obtain information on the positive or negative polarity of these opinions using the Naive Bayes and Support Vector Machine (SVM) methods to choose a more effective way in case studies to determine sentiment predictions. The steps are taken consist of data collection, processing data, weighting data, classification process, evaluation, validation, and results and discussion. The web scraping technique was used, and after going through the data cleaning stages, a total of 780 tweet data was obtained. The results of the classification test show that the SVM method has a greater accuracy rate with a proportion of 79.5% compared to the Naive Bayes method with a proportion of 72.5%. The SVM method's prediction results also show several 144 positive and 636 negative sentiments. Judging from the Wordcloud that was formed, it can be assumed that people are worried about their economic conditions, one of which is the unstable oil price which can trigger a recession.
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