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One such program is facilitated through the Cek Bansos application, which enables users to check their eligibility for social aid. However, user experiences with the application vary, leading to various sentiments in their reviews. Understanding these sentiments is crucial for improving the application’s functionality and user satisfaction. This study focuses on sentiment analysis of user reviews of the Cek Bansos application by leveraging a fine-tuned Indonesian-language Bidirectional Encoder Representations from Transformers (BERT) model. This research aims to evaluate the BERT model’s effectiveness in classifying sentiments in user reviews and provide insights that could improve the Cek Bansos application. This research method is the BERT model was fine-tuned using hyperparameters such as a learning rate of 3e-6, batch size of 16, and 9 epochs. The dataset consisted of 8,000 reviews, divided into training (70%), validation (20.1%), and test (9.9%) sets. Review scores were manually categorized, where ratings of 1 to 2 were classified as negative sentiment, 3 as neutral, and 4 to 5 as positive. The results of this research are as follows: the fine-tuned model achieved an accuracy of 77%, with additional evaluation metrics such as precision, recall, and F1 score, demonstrating the model’s effectiveness in identifying positive, negative, and neutral sentiments separately. This study concludes that the BERT model provides a reliable method for sentiment classification of user reviews, which could support developers and policymakers in refining the Cek Bansos application to enhance user experience. Additionally, a web-based application developed using Streamlit allows government officials to visualize sentiment trends in real time, improving their understanding of user feedback. Future research could further explore alternative machine learning models and additional linguistic features to improve sentiment classification accuracy and the overall user experience.\u003Cbr>Copyright ©2025 The Authors.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr>|\n| Corresponding Author:\u003Cbr>Alam Rahmatulloh\u003Cbr>Universitas Siliwangi, Kota Tasikmalaya, Indonesia.\u003Cbr>Email: [alam@unsil.ac.id](alam@unsil.ac.id) |  |\n| How to Cite: E. Haerani, A. Rahmatulloh, and S. Elmeftahi,“Bidirectional Encoder Representations from Transformers Fine-Tuning for Sentiment Classification of Cek Bansos Reviews,” International Journal of Engineering and Computer Science Applications (IJECSA), vol. 4, no. 1, pp. 59-70, Mar. 2025. doi: 10.30812/ijecsa.v4i1.4981 . |  |\n\nJournal homepage: [https://journal.universitasbumigora.ac.id/index.php/ijecsa](https://journal.universitasbumigora.ac.id/index.php/ijecsa)  \n1. INTRODUCTION  \nMobile phone applications are becoming increasingly important in everyday life in the era of modern technology, including in government work and public services. One important application is the Social Assistance Check application, which is designed to help the public access information about social assistance. Reviews from users of this application are a valuable source of information that ca","cbCaiiRayMLvQ5JG","https://ap.wps.com/l/cbCaiiRayMLvQ5JG","pdf",531276,12,"English","# Introduction\n## Sentiment analysis gap and application context\n## Why BERT for Indonesian sentiment classification\n## Comparison with traditional machine learning approaches","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"To perform sentiment analysis on user reviews of the Cek Bansos application using a fine-tuned Indonesian BERT model and evaluate its effectiveness for sentiment classification.\"},{\"question\":\"How was the sentiment labeling and dataset split conducted?\",\"answer\":\"Ratings of 1–2 were labeled negative, 3 as neutral, and 4–5 as positive. The dataset of 8,000 reviews was split into training (70%), validation (20.1%), and test (9.9%).\"},{\"question\":\"What results were achieved and how are sentiments presented to stakeholders?\",\"answer\":\"The fine-tuned model reached 77% accuracy and is evaluated with precision, recall, and F1 score for positive, negative, and neutral classes. A Streamlit-based web application visualizes sentiment trends in real time.\"}]","International Journal of Engineering and Computer Science Applications (IJECSA) - Bidirectional Encoder Representations from Transformers Fine-Tuning for Sentiment Classification of Cek Bansos Reviews | PDF"]