Abstract
Tourism significantly contributes to regional economic growth and enhances public welfare. Baturraden tourist attraction, located in Banyumas Regency, Central Java, is one of the destinations whose main attraction is nature tourism. Data on visitors to Baturraden tourist attraction over the past few years shows a good trend. To ensure long-term sustainability and enhance service quality, understanding visitor perceptions and experiences is crucial. This study employs Aspect-Based Sentiment Analysis (ABSA) to analyze visitor reviews of Baturraden. Utilizing the IndoBERT model, a deep learning architecture based on Bidirectional Encoder Representations from Transformers (BERT) specifically tailored for the Indonesian language, the research focuses on four key aspects: Attraction, Accessibility, Amenities, and Ancillary Services. Next stage, a pre-processing process is carried out which includes Case Folding, Cleansing, Tokenizing, Normalization, Stemming and Stopword. Model evaluation is conducted using a confusion matrix, assessing accuracy (94.61%), precision (83.22%), recall (96%), and F1-score (88.11). These results demonstrate the model's can classif reviews into the required aspects.A primary challenge encountered in this research involves analyzing reviews exhibiting diverse linguistic styles, including variations in language and dialect, as well as addressing the issue of imbalanced data distribution across the different aspects.
Published Version
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