Analisis Sentimen Publik terhadap Virus HMPV Berdasarkan Media Sosial X dengan Algoritma Logistic Regression - Wijaya, Feri Aldi; Parjito, Parjito (Forum Kerjasama Pendidikan Tinggi, 2025)
Human Metapneumovirus (HMPV) is a virus that affects the respiratory tract, causing flu-like symptoms such as cough, fever, and nasal congestion. This virus was first discovered in 2001 and generally causes mild infections. However, certain groups, such as children, the elderly, and individuals with weakened immune systems, are at higher risk of developing severe conditions like bronchitis or pneumonia. Based on this issue, a sentiment analysis of public responses to Human Metapneumovirus (HMPV) cases was conducted using data collected from the X platform, consisting of 10,199 tweets. The data was gathered between December 1, 2024, and January 30, 2025, using Tweet Harvest in Google Colab with the Twitter API. This study applied the Synthetic Minority Oversampling Technique (SMOTE) to address data imbalance, with an 80% to 20% split between training and testing data. The results showed that before applying SMOTE, the logistic regression algorithm had an accuracy of 83%, with precision for positive sentiment at 90%, neutral at 80%, negative at 85%, while recall for positive sentiment was 89%, neutral 89%, negative 92%. After applying SMOTE, accuracy increased to 90%, with the most significant improvement observed in positive sentiment. The precision for positive sentiment reached 90%, neutral 87%, and negative 95%, while recall for positive sentiment was 96%, neutral 90%, negative 84%. This research provides insights into the use of logistic regression algorithms in sentiment analysis related to HMPV and serves as a reference for governments and health organizations in designing more effective communication strategies and interventions.