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All Journal Jurnal Transformatika Sistemasi: Jurnal Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Jurnal Sosial dan Teknologi JUSTIN (Jurnal Sistem dan Teknologi Informasi) Melek IT: Information Technology Journal The Indonesian Journal of Computer Science Research
Frobenius, Arvin Claudy
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Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice

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Comparative Analysis of BERT and LSTM Models for Sentiment Classification of Mobile Game User Reviews Indriyatmoko, Toto; Rahardi, Majid; Utama, Hastari; Frobenius, Arvin Claudy
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12149

Abstract

Sentiment classification of user reviews for mobile games that rely on direct advertising (direct ads) is crucial for understanding player perceptions and improving user experience. This study aims to compare the performance of two deep learning architectures, Long Short-Term Memory (LSTM) and multilingual Bidirectional Encoder Representations from Transformers (BERT) in classifying sentiment in reviews into three categories, positive, negative, and neutral. The dataset used consists of reviews from games employing direct ads, which underwent rule-based labeling and text preprocessing. The LSTM model was built from scratch using a custom embedding layer, while the multilingual BERT model was fine-tuned using a transfer learning approach. Evaluation was conducted based on accuracy, precision, recall, and F1-score metrics. Experimental results show that multilingual BERT achieves superior validation loss compared to LSTM (0.37 vs. 0.44). BERT also outperforms LSTM significantly in terms of F1-score and its ability to understand multilingual linguistic context. However, LSTM demonstrates advantages in computational efficiency and training speed. These findings offer practical recommendations for developers in selecting an appropriate sentiment analysis model based on accuracy requirements and resource availability.
Co-Authors Andayani, Ririn D, Yayang Akbar Fathony, Zamzam Hakim, Muhammad Irvan Hutabriandari, Oktaviana Wulan Indriyatmoko, Toto Jeki Kuswanto Khamzah , Khaerul Kharomadhona, Nurdini Khoirunnisa, Nur Andini Kurniawan, Rizki Candra Kusumaningrum, Andi Sutra Majid Rahardi Majid, Ammar Waly Pramuji, Oktario Ronny Rifda Faticha Alfa Aziza Saputra, Eko Rachmat Slamet .H Satheno, Gabriel Christian Ilham Shonta, Angga Arindra Utama, Hastari Wibowo, Danang Saputro wisnu yudo untoro, wisnu