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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi
Hilya Tsaniya
Institut Teknologi Sepuluh Nopember
Computer Science & IT Dentistry Education Library & Information Science

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SentiBERT and Enhanced Bi-GRU for Weather-related Text Classification Using Lexical Features Mohamad Anwar Syaefudin; Arijal Ibnu Jati; Hilya Tsaniya; Chastine Fatichah; Diana Purwitasari
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 1, January 2026
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i1.a1320

Abstract

The growing volume of weather related content on social media platforms, especially Twitter, has highlighted the need for robust classification models that can handle noisy, ambiguous, and emotionally subtle language. However, existing models machine learning such as Support Vector Machines (SVM) often fail to effectively capture implicit sentiment and sequential context in short, real time texts. This study addresses the challenge of weather related text classification by proposing a hybrid architecture that combines SentiBERT, a sentiment aware transformer model, with an Enhanced BiGRU network equipped with Self Attention and LeakyReLU activation. Experiments were conducted using a five class(sunny, cloudy, rainy, extreme, other) dataset of weather related tweets with stratified cross validation across multiple deep learning models and tokenizers. Results show that the proposed SentiBERT + Enhanced BiGRU model outperformed all baselines, achieving 88.03% accuracy and 88.25% macro F1 score demonstrating its ability to better interpret contextual and emotional nuances. These findings imply that integrating sentiment specific embeddings with sequential modeling and lexical features offers a promising direction for future real time applications in climate monitoring and disaster alert systems.
Handling Ambiguity in App Review-Based Software Requirement Classification Using Multi-Label BERT Transfer Learning Stefani Tasya Hallatu; Muhammad Jerino Gorter; Andrea Bemantoro J; Diana Purwitasari; Chastine Fatichah; Hilya Tsaniya
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 1, January 2026
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i1.a1333

Abstract

User-generated reviews on mobile applications represent a valuable yet ambiguous resource for classifying software requirements, particularly when multiple aspects—such as bugs, feature requests, and user experiences—are embedded within a single review. Although prior studies have shown the potential of transformer-based and multi-label models in improving text classification accuracy and efficiency, explicit handling of semantic ambiguity in multi-aspect reviews has not been addressed. This study proposes a multi-label classification approach using BERT-based transfer learning to manage ambiguity in app reviews. Each review is manually annotated with one or more relevant requirement categories. Preprocessing involves text cleaning, normalization, and BERT tokenization to convert reviews into structured representations. The classification model categorizes reviews into four classes: bug reports, feature requests, user experiences, and ratings. Evaluation results demonstrate strong performance, with F1-scores of 0.96 for bug reports, 0.95 for feature requests, 0.97 for ratings, and 0.80 for user experiences, confirming the model’s capability in capturing overlapping labels in ambiguous reviews. This approach offers a scalable and automated solution for extracting software requirements, enabling developers to better identify, categorize, and prioritize user needs from unstructured review data.
Co-Authors Andrea Bemantoro J Arijal Ibnu Jati Chastine Fatichah Diana Purwitasari Mohamad Anwar Syaefudin Muhammad Jerino Gorter Stefani Tasya Hallatu