Implementation of IndoBERT for Sentiment Analysis of Indonesian Presidential Candidates - Primanda Sayarizki; Hasmawati; Hani Nurrahmi (School of Computing, Telkom University, 2024)
In this modern era, Indonesian society widely utilizes social media, particularly Twitter, as a means to express their opinions. Every day, various opinions of Indonesian citizens are disseminated on this platform, including their views on prospective presidential candidates for the year 2024. Analyzing public opinions regarding prospective presidential candidates in 2024 is crucial to understanding the sentiment of the people toward these candidates. Such sentiment analysis can be conducted using deep learning techniques such as IndoBERT to acquire knowledge regarding the classification of sentiments as positive, neutral, or negative. IndoBERT is employed to generate vector representations that encapsulate the meaning of tokens, words, phrases, or texts. These representation vectors can then be input into a classification model to perform sentiment analysis. The sentiment classification model undergoes testing with a diverse set of tweets in the test dataset, which represent a wide range of public opinions. The evaluation results indicate an overall accuracy rate of 80%, with precision rates of 62% for negative sentiment, 81% for neutral sentiment, and 85% for positive sentiment. Additionally, the recall rates for each sentiment are 64% for negative, 81% for neutral, and 84% for positive, with corresponding F1-scores of 63%, 81%, and 85%, respectively.