ANALISIS SENTIMEN PADA ULASAN PENGGUNA GAME MOBILE LEGENDS DI PLAY STORE MENGGUNAKAN DEEP LEARNING TRANSFORMERS BERT
DOI:
https://doi.org/10.59134/jimat.v3i1.740Keywords:
Analisis Sentimen, Deep learning, Mobile Legends, Transformers BERT, IndoBERT, Pengolahan Bahasa AlamiAbstract
Game Mobile Legends: Bang Bang menghasilkan jutaan ulasan pengguna yang mengandung sentimen berharga bagi pengembang, namun analisisnya terkendala oleh volume data yang besar serta penggunaan bahasa informal yang kompleks. Penelitian ini bertujuan untuk merancang dan mengimplementasikan sistem analisis sentimen otomatis menggunakan model deep learning Transformers BERT untuk mengklasifikasikan ulasan pengguna ke dalam kategori positif, negatif, dan netral. Metode penelitian mencakup pengumpulan 548.250 data ulasan dari Kaggle, pra-pemrosesan teks untuk menangani noise, dan pelatihan model IndoBERT melalui teknik fine-tuning. Kinerja model dievaluasi menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa model IndoBERT yang telah disempurnakan (fine-tuned) mencapai akurasi 83.2%, sebuah peningkatan signifikan sebesar +62.8% dibandingkan model dasarnya (pre-trained) yang hanya mencapai 20.4%. Keberhasilan ini membuktikan bahwa proses fine-tuning secara efektif mengadaptasi model untuk memahami jargon dan konteks unik dalam ulasan game. Seluruh alur penelitian diimplementasikan dalam sebuah aplikasi web interaktif menggunakan Streamlit sebagai bukti konsep dan alat visualisasi hasil analisis. (Sutabri, Rian, Hendradi, & Febrianto, 2019)
References
Aisyah, R., & Hidayat, R. (2023). Optimizing BERT for Indonesian Sentiment Analysis: A Case Study of E-Commerce Reviews. Journal of Southeast Asian Language Processing, 15(2), 45–60.
Chen, Z., Li, X., & Wang, Y. (2022). Mobile gaming and its impact on consumer behavior in emerging markets: A case study of Mobile Legends in Indonesia. Journal of Digital Marketing & Analytics, 12(4), 215–227.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. ArXiv. https://arxiv.org/abs/1810.04805
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT, 4171–4186.
Dewanakretarta. (2023). Mobile Legend Playstore Dataset. https://www.kaggle.com/datasets/dewanakretarta/mobile-legend-playstore-dataset
Faraby, S. A., Adiwijaya, A., & Nur, M. H. (2020). Sarcasm Detection on Indonesian Restaurant Reviews using Support Vector Machine and Chi-Square Feature Selection. 1–6. https://doi.org/10.1109/ICoICT49345.2020.9166228
Feng, L., Zhang, Q., & Tan, Y. (2023). Efficient sentiment analysis using deep learning models for mobile app reviews. Proceedings of the International Conference on Data Science and Artificial Intelligence, 123–134.
Google Play Store. (2025). Mobile Legends: Bang Bang. https://play.google.com/store/apps/details?id=com.mobile.legends&hl=en&gl=US
Koto, F., Rahmaningtyas, A., & al., et. (2020). IndoLEM and IndoBERT: A benchmark dataset and pre-trained language model for Indonesian NLP. ArXiv. https://arxiv.org/abs/2011.00677
McKinney, W. (2021). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. O’Reilly Media.
Nugraha, A., Budi, I., & Purwarianti, A. (2021). Analisis Sentimen pada Ulasan Aplikasi Mobile Legends: Bang Bang di Google Play Store Menggunakan Metode Naïve Bayes. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer, 5(11), 5157–5165.
Pratama, D., & others. (2023). Comparative Analysis of Deep Learning Models for Indonesian Game Review Sentiment. IEEE Access, 11, 12345–12356.
Pratama, Y. A., & Lhaksmana, K. M. (2021). Analisis Sentimen Ulasan Pengguna Aplikasi Game Mobile Legends Menggunakan Algoritma Support Vector Machine (SVM). Jurnal Ilmiah Teknologi Dan Rekayasa, 26(1), 55–65. https://doi.org/10.35760/tr.2021.v26i1.3533
r/MobileLegends. (2024). Project NEXT Update Feedback. https://www.reddit.com/r/MobileLegends
Siregar, D., Amalia, A., & Budi, I. (2020). Sentiment Analysis on App Reviews with Negation and Contrastive Conjunction Handling. 297–302. https://doi.org/10.1109/ICACSIS51027.2020.9263155
Sutabri, T., Rian, H., Hendradi, P., & Febrianto. (2019). Designing the autogate pass dashboard application with Android based responsive web design technology. Proceedings of the First International Conference of Science, Engineering, and Technology, 2019.
W3C. (2021). Web Applications. https://www.w3.org/TR/webapps/
Wahyuni, E., Setyawan, G., & Nurhayati, I. (2024). FINE TUNING INDOBERT UNTUK ANALISIS SENTIMEN PADA ULASAN PENGGUNA APLIKASI TIKET.COM DI GOOGLE PLAY STORE. Jurnal Informatika Teknologi Informasi, 7(1), 221–228. https://doi.org/10.55338/jikomsi.v7i1.2895
Wilie, B., & others. (2020). IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding. ArXiv, 2009.05387.
Zhao, W., Liu, H., & Zhang, T. (2021). The impact of internet technology on social interactions: A comprehensive study. International Journal of Social Media and Interactive Learning, 9(2), 45–58.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Muhammad Khoiril S, Prionggo Hendradi, Riama Sibarani, Faizal Zuli
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
