Sentiment Analysis Using Stacking Ensemble After the 2024 Indonesian Election Results - Andy Victor Pakpahan; Fahmi Reza Ferdiansyah; Robby Gustian; Muhammad Nur Faiz; Sukma Aji (Journal of Innovation Information Technology and Application)
Sentiment analysis is a text processing technique aimed at identifying opinions and emotions within a sentence. Machine learning is commonly applied in this area, with algorithms such as Naïve Bayes, Support Vector Machine (SVM), and Random Forest being frequently used. However, achieving optimal accuracy remains a challenge, particularly when dealing with unstructured text data, such as content from social media platforms. This research seeks to improve sentiment analysis performance by implementing a stacking ensemble learning approach, which combines the predictive strengths of several base models. The base models selected for this study are Naïve Bayes, SVM, and Random Forest, while Random Forest also serves as the meta-model to generate final predictions.