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Imamah Imamah
Department of Information Systems, University of Trunojoyo Madura, Bangkalan, Indonesia
Computer Science & IT

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Improving RoBERTa Performance through Hyperparameter Optimization for Sentiment Analysis of Indonesian Tourism Reviews Imamah Imamah; Myo Thida; Fika Hastarita Rachman; Budi Dwi Satoto; Sri Herawati; Yeni Kustiyahningsih; Eka Mala Sari Rochman; Meita Lailatuz Zakiyah
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5672

Abstract

The performance of transformer models such as RoBERTa in sentiment classification is influenced by hyperparameter settings, especially the epoch and batch sizes. However, no previous study has examined the impact of changes in the number of epochs and batch sizes on the performance of each class in classification tasks, especially in Indonesian-language sentiment analysis of tourism reviews. Therefore, this study aims to fill this gap by analyzing the performance of RoBERTa and the impact of various hyperparameter settings on sentiment for each class. The dataset consists of 3,875 reviews from visitors to Lake Sarangan on Google Maps. The batch sizes used in this study are 8 and 16, and the epoch range is 2 to 4. There are three classes of sentiment: negative, neutral, and positive. The results demonstrate that increasing the batch size from 8 to 16 does not linearly improve model performance. The optimal combination of epoch=4 and batch size=8 achieved 91% accuracy, with significant improvements in recall and F1-score across all classes, especially in positive sentiment classification. This research offers valuable insights into fine-tuning RoBERTa for sentiment analysis in Indonesian contexts, providing recommendations for future sentiment analysis tasks in natural language processing.
Co-Authors Budi Dwi Satoto Eka Mala Sari Rochman Fika Hastarita Rachman Meita Lailatuz Zakiyah Myo Thida Sri Herawati Yeni Kustiyahningsih