Analisis Sentimen Pengguna Twitter Terhadap Pemilu 2024 Berbasis Model XLM-T - Ghufron, Mochamad Rafli; Mahabbataka Arsyada, Muhammad Farrih; Lukman, Muhammad Rizano; Haryono Putra, Yudhistira Azhar; Rakhmawati, Nur Aini (LPPM STIKI MALANG, 2023)
In the current digital era, social media, especially Twitter, has become an important platform for people to share opinions, especially regarding political issues such as the 2024 Presidential Election (Pemilu) in Indonesia. This research aims to analyze public sentiment regarding the 2024 Election based on collected tweet data. By using an XLM-T based machine learning model, this research succeeded in classifying tweets into three sentiment categories: positive, negative and neutral with a model accuracy rate of 68%. The results show that tweets with positive and negative sentiments receive more interaction from the public compared to tweets with neutral sentiments, indicating the public's tendency to more actively interact with opinions that have a certain position or stance on an issue. In conclusion, sentiment analysis can provide deep insight into the public's views on the 2024 Election, which political stakeholders can utilize in designing their campaign strategies.