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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JURIKOM (Jurnal Riset Komputer) Journal of Soft Computing Exploration Melek IT: Information Technology Journal Jurnal Informatika: Jurnal Pengembangan IT Lex Scientia Law Review Prosiding Seminar Nasional CORISINDO
Fitriani, Maulida Ayu
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice

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Journal : Melek IT: Information Technology Journal

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Aspect Based Sentiment Analysis Menggunakan Indobert Model Terhadap Review Pengunjung Objek Wisata Baturraden Febrianto, Dany Candra; Fitriani, Maulida Ayu; Afrad, Mahazam; Khadija, Mutiara Auliya
Melek IT : Information Technology Journal Vol. 10 No. 2 (2024): Melek IT: Information Technology Journal
Publisher : Informatics Department-Universitas Wijaya Kusuma Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30742/melekitjournal.v10i2.358

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.
Co-Authors Cahyadri, Rizky Aulia Damarsiwi, Dyah Kartika Dany Candra Febrianto Dimara Kusuma Hakim Feri Wibowo Ghifari, Abu Dzar Al Hindayati Mustafidah Hindayati, Mustafidah Indriati Amarini Ismail, Noorfajri Khadija, Mutiara Auliya Mahazam Afrad Muhammad Hamka Nugroho, Aswin Mulyo pambudi, elindra ambar Sidqiyah, Elis Dhia Supriyono Supriyono Widyantri, Shafrila