Analisis Sentimen Purbaya Effect pada X Menggunakan VADER Sentiment, SVM, dan SMOTE

Abstract

The rapid growth of social media as a platform for public expression has generated a substantial volume of unstructured opinion data, creating the need for computational-based analytical approaches. One phenomenon that attracted public attention on platform X is the Purbaya Effect, which triggered various public responses reflected in positive, neutral, and negative sentiments. This study aims to analyze public sentiment toward this phenomenon by integrating VADER Sentiment, Support Vector Machine (SVM), and Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance in the dataset. This research employed a quantitative approach based on text mining using 2,871 Indonesian-language tweets collected from platform X during the period of October 2025 to May 2026. The research procedure consisted of data collection, preprocessing (cleaning, case folding, tokenization, and stopword removal), sentiment labeling using VADER, data balancing using SMOTE, sentiment classification using SVM, and model evaluation through accuracy, precision, recall, and F1-score metrics. The results indicate that the sentiment distribution was dominated by the neutral category, while positive and negative sentiments appeared in considerably smaller proportions. The SVM model before applying SMOTE achieved an accuracy of 91.80%, but demonstrated a strong bias toward the majority class, resulting in limited capability to identify minority classes. After implementing SMOTE, the dataset became more balanced and the model’s ability to detect minority classes improved, as reflected by increased recall and F1-score values, although the overall accuracy decreased to 77.87%. These findings suggest that applying SMOTE improves the quality of multiclass sentiment classification and provides a more representative evaluation than relying solely on accuracy as a performance indicator.