Klasifikasi Sentimen Untuk Mengetahui Kecenderungan Politik Pengguna X Pada Calon Presiden Indonesia 2024 Menggunakan Metode IndoBert
-
Indro Abri Oktariansyah
*
Universitas Jenderal Achmad Yani,
Indonesia
- Fajri Rakhmat Umbara Universitas Jenderal Achmad Yani, Indonesia
- Fatan Kasyidi Universitas Jenderal Achmad Yani, Indonesia (*) Corresponding Author
Abstract
X has evolved into one of the most popular social media platforms in the world. In Indonesia, the use of X is quite widespread, especially in discussions about the presidential election, which is currently a hot topic. Everyone has different views on the candidates, both positive and negative. With a large amount of tweet data from users, this information can serve as a data source for processing and analysis. Various methods can be used to analyze and classify sentiment from this data, one of which is using BERT. This research conducts sentiment classification using BERT with the IndoBert model. The research aims to classify sentiments towards tweets related to the 2024 Indonesian presidential election to understand the political inclinations of X users, evaluate the performance of the IndoBert model in sentiment classification, and assess the extent to which back translation augmentation and synonym augmentation techniques can enhance the model's performance. Data was collected using crawling techniques for seven days leading up to the election and manually labeled by annotators. Synonym augmentation and back translation techniques were used to balance data in minority classes. The data was divided into 80% training data, 10% test data, and 10% validation data. The classification process was conducted using the IndoBert model that had been fine-tuned. The research results show that IndoBert with synonym augmentation achieved the highest accuracy, which was 82% in the first experiment and 81% in the second experiment. On the other hand, back translation only reached an accuracy of 78% in the first experiment and 74% in the second experiment. This indicates that synonym augmentation proved to be more effective in increasing data variation and model performance on the dataset used in this research.
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