<![CDATA[Stance detection in COVID-19 vaccination utilizing tweets is crucial for several reasons, such as public health communication, monitoring vaccine sentiment, and identifying misinformation. This research aims to explore the use of attention-based neural networks for stance detection in Indonesian COVID-19 vaccination tweets. The research focuses on enhancing accuracy by integrating content and network features. The content features represent the tweet's text, while network features define the user account's following or unfollowing. The primary contribution of this research is the development of an Attention Long Short-Term Memory (AttLSTM) model for stance detection in Indonesian tweets related to the COVID-19 vaccination. This model combines content and network features to improve accuracy in classifying user attitudes. We also highlight the performance differences between Word2Vec and FastText for numerical text representation in the AttLSTM model. The research used the Indonesian COVID-19 vaccination-related tweet dataset from prior research. The dataset is extracted using user metadata to obtain content and network features necessary to represent users' interest in tweets. Our research method involves data preparation, preprocessing, extraction of content and network features, and the development of an AttLSTM model. By integrating content and network features into the AttLSTM model with Word2Vec text representation, the study demonstrates superior performance compared to the LSTM baseline model and FastText. Adding attention mechanisms to the baseline LSTM model can capture crucial information, such as the minority class inside a tweet's text. Future research will involve exploring advanced data processing methods and ensemble learning techniques to further improve the model's performance.]]>
