Perbandingan Metode Naive Bayes dan Support Vector Machine Untuk Analisis Sentimen Pengguna X Terhadap Penyakit Virus HMPV di Indonesia
Keywords:
Sentiment Analysis, HMPV, Naïve Bayes, SVM, IndoBERTAbstract
Human Metapneumovirus (HMPV) is a respiratory virus from the Pneumoviridae family that can infect both the upper and lower respiratory tract. Although no official cases have been identified in Indonesia, the spread of HMPV in several countries has raised public concern. Social media, particularly platform X (formerly Twitter), has become a primary platform for the public to express opinions, information, and discussions related to health issues. Sentiment analysis is needed to understand public perceptions, thus providing a basis for the government and medical professionals to formulate more appropriate communication strategies. This study aims to classify public sentiment regarding HMPV into positive, negative, and neutral categories and to compare the performance of two text classification methods: Naïve Bayes and Support Vector Machine (SVM). The research methodology uses the CRISP-DM framework with the following stages: data collection of 1,476 tweets using keywords related to HMPV, text preprocessing (cleaning, case folding, tokenizing, filtering, stemming), automatic labeling using IndoBERT, data balancing through resampling, and feature extraction with TF-IDF. The data was then split into training-test ratios (90:10, 80:20, 70:30, 60:40), followed by modeling and evaluation using a confusion matrix, accuracy, precision, recall, and F1-score. The results showed that SVM outperformed Naïve Bayes, with higher accuracy and F1-score in almost all data split scenarios. These findings confirm that SVM is superior in analyzing the sentiment of Indonesian-language text on social media. This research is expected to support public opinion monitoring, strengthen health communication strategies, and serve as a reference for further research on other health issues.
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