Analisis Sentimen Masyarakat Indonesia terhadap Keterlibatan Bill Gates dalam Program Vaksin TBC di Media Sosial X Menggunakan SVM
DOI:
https://doi.org/10.30865/jurikom.v13i1.9431Keywords:
Sentiment Analysis, TB Vaccine, Bill Gates, Support Vector Machine, TF-IDFAbstract
The involvement of Bill Gates in the development program of the tuberculosis (TB) vaccine has generated diverse responses among the Indonesian public, particularly as expressed through social media platform X, making it an important issue to examine since public sentiment may influence public trust in health programs and the success of TB prevention efforts. This study aims to analyze public sentiment in Indonesia toward Bill Gates’ involvement in the TB vaccine program based on social media data from platform X using the Support Vector Machine (SVM) algorithm. The research data consist of 784 Indonesian-language tweets collected through web scraping techniques using keywords related to the TB vaccine issue and Bill Gates. The collected data then underwent several text preprocessing stages, including data cleaning, case folding, tokenization, word normalization, stopword removal, and stemming to improve text quality and consistency. Feature representation was performed using the Term Frequency–Inverse Document Frequency (TF-IDF) method, while the dataset was split into 80% training data and 20% testing data. The classification model was built using an SVM algorithm with a linear kernel to optimally separate positive and negative sentiment classes. The experimental results show that the SVM model combined with TF-IDF achieved an accuracy of 96.8%, with a precision of 98.3%, a recall of 79.2%, and an F1-score of 86.0%. Sentiment distribution analysis indicates that the majority of tweets were dominated by negative sentiment, while positive sentiment appeared in a smaller proportion. The main contribution of this study lies in the application of social media–based sentiment analysis specifically to the TB vaccine issue associated with a global public figure in the Indonesian context, as well as in demonstrating that the combination of SVM and TF-IDF is effective in accurately classifying public opinion. These findings are expected to serve as a data-driven reference for government institutions and public health stakeholders in designing more effective public communication strategies.
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