Muhammad Khoiril Saipul Primary Author mixed material bibliography BEKASI Universitas Satya Negara Indonesia 2025 id Indonesia

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The game Mobile legends: Bang Bang generates millions of user reviews containing valuable sentiment for developers, but its analysis is hindered by large data volumes and the use of complex informal language. This research aims to design and implement an automated sentiment analysis system using the deep learning Transformers BERT model to classify user reviews into positive, negative, and neutral categories. The research method includes collecting 548,250 review data from Kaggle, text preprocessing to handle noise, and training the IndoBERT model through a finetuning technique. The model's performance was evaluated using accuracy, precision, recall, and F1-score metrics. The results indicate that the fine-tuned IndoBERT model achieved an accuracy of 83.2%, a significant improvement of +62.8% compared to the pre-trained base model, which only reached 20.4%. This success demonstrates that the fine-tuning process effectively adapts the model to understand the unique jargon and context of game reviews. The entire research workflow is implemented in an interactive web application using Streamlit as a proof of concept and a tool for visualizing the analysis results. Muhammad Khoiril Saipul Teknik Informatika Mobile Legends Sentiment Analysis Deep learning Transformers BERT IndoBERT Natural Language Processing TI 2025 Institutional Repository USNI Universitas Satya Negara Indonesia TI 2025 8250233 Perpustakaan USNI Kampus B (SKRIPSI) TI 2025 ANALISIS SENTIMEN PADA ULASAN PENGGUNA GAME MOBILE LEGENDS DI PLAY STORE MENGGUNAKAN DEEP LEARNING TRANSFORMERS BERT 4286 2025-09-17 13:40:41 2025-09-18 14:25:37 machine generated