Perbandingan Model Machine Learning dalam Analisis Sentimen Pada Kasus Monkeypox di Media Sosial X

Monkeypox or MPOX, is a zoonotic disease caused by the monkeypox virus, a member of the genus Orthopoxvirus. Monkeypox became a global concern after cases of transmission were reported in various countries, sparking widespread discussion on social media X. This platform is often used by the public to disseminate information and express concerns related to the disease. This study aims to compare the performance of several models in sentiment analysis related to the Monkeypox case on social media X. The models tested include Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes, and Random Forest (RF). The data used consisted of tweets containing opinions or information about Monkeypox, which were then processed through the stages of text normalization, remove stopwords, and stemming. Furthermore, feature weighting was carried out using the TF-IDF technique and feature selection using the Chi-Square method, resulting in an optimal number of features of 652. The results of the analysis show that SVM provides the highest accuracy of 83%, with a 3% increase from the previous number of features, which was 500. Although KNN and Naïve Bayes showed significant improvements, Random Forest did not experience any significant changes in their performance. The study concluded that SVM is the most effective model in analyzing Monkeypox-related sentiment on social media X. For future research, it is recommended to explore deep learning techniques and the use of larger datasets to improve the accuracy and depth of sentiment analysis.