PENERAPAN MODEL TRANSFORMER INDOBERT UNTUK ANALISIS SENTIMEN PADA ULASAN APLIKASI TWITCH
DOI:
https://doi.org/10.31849/nw62kn64Keywords:
Analisis Sentimen, IndoBERT, Transformer, Ulasan Aplikasi, TwitchAbstract
Pemahaman terhadap persepsi publik terhadap aplikasi Twitch menjadi penting karena ulasan pengguna mencerminkan pengalaman penggunaan, kualitas layanan, serta berbagai permasalahan yang dihadapi pengguna. Penelitian ini bertujuan untuk menganalisis sentimen ulasan pengguna aplikasi Twitch berbahasa Indonesia dengan menerapkan model Transformer IndoBERT (indobert-base-p1) melalui proses fine-tuning. Dataset diperoleh dari Google Play Store menggunakan google-play-scraper, kemudian dipraproses melalui cleaning, case folding, normalisasi, tokenisasi, stopword removal, dan stemming. Pelabelan data dilakukan menggunakan pendekatan lexicon-based sebagai ground truth dengan tiga kelas sentimen, yaitu positif, netral, dan negatif. Selanjutnya data digunakan untuk melatih model IndoBERT dengan konfigurasi learning rate 2e-5, batch size 8, dan lima epoch. Evaluasi dilakukan menggunakan accuracy, precision, recall, dan F1-score. Hasil pengujian menunjukkan bahwa model terbaik diperoleh pada epoch ke-4 dengan akurasi sebesar 89% dan weighted average F1-score sebesar 89%. Analisis confusion matrix memperlihatkan bahwa model memiliki performa terbaik pada kelas negatif dan netral, sedangkan kelas positif masih mengalami sejumlah kesalahan klasifikasi ke kelas netral. Temuan ini menunjukkan bahwa IndoBERT mampu memahami konteks bahasa informal pada ulasan aplikasi berbahasa Indonesia dan layak digunakan sebagai pendekatan analisis sentimen untuk mendukung evaluasi kualitas layanan Twitch.
References
[1] F. Koto, A. Rahimi, J. H. Lau, and T. Baldwin, “IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP,” COLING 2020 - 28th Int. Conf. Comput. Linguist. Proc. Conf., pp. 757–770, 2020, doi: 10.18653/v1/2020.coling-main.66.
[2] K. S. Nugroho, A. Y. Sukmadewa, H. Wuswilahaken DW, F. A. Bachtiar, and N. Yudistira, “BERT Fine-Tuning for Sentiment Analysis on Indonesian Mobile Apps Reviews,” in 6th International Conference on Sustainable Information Engineering and Technology 2021, New York, NY, USA: ACM, Sep. 2021, pp. 258–264. doi: 10.1145/3479645.3479679.
[3] S. Apriliani, A. Erfina, and C. Warman, “Fine-Tuned IndoBERT for Aspect-Based Sentiment Analysis of Indonesian Five-Star Hotel Reviews,” vol. 14, pp. 437–445, 2025.
[4] A. N. Sianipar, Yuhelmi, and M. Devega, “ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI DANA DAN LINKAJA MENGGUNAKAN METODE NAÏVE BAYES,” Zo. J. Sist. Inf., vol. 6, no. 2, pp. 510–522, Jun. 2024, doi: 10.31849/zn.v6i2.19907.
[5] R. Kusumaningrum, I. Z. Nisa, R. Jayanto, R. P. Nawangsari, and A. Wibowo, “Deep learning-based application for multilevel sentiment analysis of Indonesian hotel reviews,” Heliyon, vol. 9, no. 6, p. e17147, 2023, doi: 10.1016/j.heliyon.2023.e17147.
[6] H. Imaduddin, F. Y. A’la, and Y. S. Nugroho, “Sentiment Analysis in Indonesian Healthcare Applications using IndoBERT Approach,” Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 8, pp. 113–117, 2023, doi: 10.14569/IJACSA.2023.0140813.
[7] A. Algiffary and T. Sutabri, “Indonesian Journal of Computer Science,” Indones. J. Comput. Sci., vol. 12, no. 2, pp. 284–301, 2023, [Online]. Available: http://ijcs.stmikindonesia.ac.id/ijcs/index.php/ijcs/article/view/3135
[8] S. A. P. Perdana, T. Bharata Aji, and R. Ferdiana, “Aspect Category Classification dengan Pendekatan Machine Learning Menggunakan Dataset Bahasa Indonesia (Aspect Category Classification with Machine Learning Approach Using Indonesian Language Dataset),” J. Nas. Tek. Elektro dan Teknol. Inf. |, vol. 10, no. 3, pp. 229–235, 2021.
[9] X. Shu, Y. Yang, and B. Wu, “Adaptive segmentation model for liver CT images based on neural network and level set method,” Neurocomputing, vol. 453, pp. 438–452, Sep. 2021, doi: 10.1016/j.neucom.2021.01.081.
[10] A. N. Ma’aly, D. Pramesti, A. D. Fathurahman, and H. Fakhrurroja, “Exploring Sentiment Analysis for the Indonesian Presidential Election Through Online Reviews Using Multi-Label Classification with a Deep Learning Algorithm,” Inf., vol. 15, no. 11, pp. 1–33, 2024, doi: 10.3390/info15110705.
[11] Y. Dou, Y. Yun, Q. Gao, and X. Zhang, “Self-representation and matrix factorization based multi-view clustering,” Neurocomputing, vol. 459, pp. 395–407, Oct. 2021, doi: 10.1016/j.neucom.2021.06.092.
[12] S. Minaee, N. Kalchbrenner, E. Cambria, N. Nikzad, M. Chenaghlu, and J. Gao, “Deep Learning--based Text Classification,” ACM Comput. Surv., vol. 54, no. 3, pp. 1–40, Apr. 2022, doi: 10.1145/3439726.
[13] M. B. M. Amin et al., “Deteksi Spam Berbahasa Indonesia Berbasis Teks Menggunakan Model Bert,” J. Teknol. Inf. dan Ilmu Komput., vol. 11, no. 6, pp. 1291–1302, 2024, doi: 10.25126/jtiik.2024118121.
[14] S. Development, G. Using, and T. Learning, “JURNAL RESTI Automatic Classification of Multilanguage Scientific Papers to the,” vol. 9, no. 3, pp. 604–612, 2025.
[15] C. J. L. Tobing, IGN Lanang Wijayakusuma, and Luh Putu Ida Harini, “Perbandingan Kinerja IndoBERT dan MBERT Untuk Deteksi Berita Hoaks Politik dalam Bahasa Indonesia,” JST (Jurnal Sains dan Teknol., vol. 14, no. 1, pp. 114–123, 2025, doi: 10.23887/jstundiksha.v14i1.92126.
[16] M. N. Zaidan, Y. Sibaroni, and S. S. Prasetyowati, “LEARNING RATE AND EPOCH OPTIMIZATION IN THE FINE-TUNING PROCESS FOR INDOBERT’S PERFORMANCE ON SENTIMENT ANALYSIS OF MYTELKOMSEL APP REVIEWS,” J. Tek. Inform., vol. 5, no. 5, pp. 1443–1450, Oct. 2024, doi: 10.52436/1.jutif.2024.5.5.2396.
[17] D. Al Akhdaan, Taufik Edy Sutanto, and Muhaza Liebenlito, “Confident Learning pada IndoBERT: Peningkatan Kinerja Klasifikasi Sentimen,” Indones. J. Comput. Sci., vol. 13, no. 5, pp. 8350–8359, 2024, doi: 10.33022/ijcs.v13i5.4401.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 ZONAsi: Jurnal Sistem Informasi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
CC BY-SA 4.0
Attribution-ShareAlike 4.0
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
