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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science
Aini Nabilah
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Comparing Word Representation BERT and RoBERTa in Keyphrase Extraction using TgGAT Novi Yusliani; Aini Nabilah; Muhammad Raihan Habibullah; Annisa Darmawahyuni; Ghita Athalina
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6279

Abstract

In this digital era, accessing vast amounts of information from websites and academic papers has become easier. However, efficiently locating relevant content remains challenging due to the overwhelming volume of data. Keyphrase Extraction Systems automate the process of generating phrases that accurately represent a document’s main topics. These systems are crucial for supporting various natural language processing tasks, such as text summarization, information retrieval, and representation. The traditional method of manually selecting key phrases is still common but often proves inefficient and inconsistent in summarizing the main ideas of a document. This study introduces an approach that integrates pre-trained language models, BERT and RoBERTa, with Topic-Guided Graph Attention Networks (TgGAT) to enhance keyphrase extraction. TgGAT strengthens the extraction process by combining topic modelling with graph-based structures, providing a more structured and context-aware representation of a document’s key topics. By leveraging the strengths of both graph-based and transformer-based models, this research proposes a framework that improves keyphrase extraction performance. This is the first to apply graph-based and PLM methods for keyphrase extraction in the Indonesian language. The results revealed that BERT outperformed RoBERTa, with precision, recall, and F1-scores of 0.058, 0.070, and 0.062, respectively, compared to RoBERTa’s 0.026, 0.030, and 0.027. The result shows that BERT with TgGAT obtained more representative keyphrases than RoBERTa with TgGAT. These findings underline the benefits of integrating graph-based approaches with pre-trained models for capturing both semantic relationships and topic relevance.
The Sentiment Analysis Of Indonesian Startup Application Reviews Using TF-IDF+SVM and FastText: A Comparative Study Aini Nabilah; Nurlayli Indah Sari; Mira Afrina; Ali Ibrahim
Journal of Information Technology and Computer Science Vol. 10 No. 3: Desember 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025103807

Abstract

The rapid rise of startups in Indonesia makes user reviews on the Google Play Store a valuable data source for understanding user perceptions and satisfaction. These unstructured reviews contain insights supporting product development and business strategies. This study analyzes sentiments in Indonesian startup app reviews and compares two classification methods: TF-IDF + Linear SVM and fastText, implemented using Google Colab. Reviews were collected in September 2025 using google-play-scraper; 4,000 reviews were retrieved and refined into 3,152 unique reviews after cleaning and preprocessing. Sentiment labeling used ratings (1–2 negative, 4–5 positive); because the neutral class was limited, this study focuses on balanced binary classification with 1576 positive and 1576 negative reviews. The process involves data scraping, text preprocessing, model training, and evaluation using accuracy, precision, recall, and F1-score metrics, with Linear SVM chosen as an efficient baseline for high-dimensional sparse TF-IDF features. Results show that fastText achieves 91.88% accuracy and an F1-macro of 0.9184, slightly outperforming TF-IDF + SVM (F1-macro 0.9103), suggesting that the embedding-based approach better captures semantic nuances of Indonesian text. Future work may extend this study to ABSA to assess sentiments toward price, UI/UX, and customer service for deeper technopreneurship insights in Indonesia.
Co-Authors Ali Ibrahim Annisa Darmawahyuni Ghita Athalina Mira Afrina Muhammad Raihan Habibullah Novi Yusliani Nurlayli Indah Sari