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Vol. 7 No. 3 (2026) • Articles
Open Access

Analisis Sentimen Teks Komentar Produk Skincare Menggunakan Model IndoBERT

Ratna Juwita
Universitas Bina Darma image/svg+xml
Heri Suroyo
Universitas Bina Darma image/svg+xml
Published: September 30, 2026 Pages: 804-812
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Abstract

The rapid growth of the local skincare industry in Indonesia has intensified consumer interactions on Instagram and TikTok, generating a substantial volume of comments that are inefficient to analyze manually. This study aims to implement the IndoBERT model to classify sentiment in beauty product comments using the CRISP-DM framework. A total of 5,062 comments from five local brands (Somethinc, MS Glow Beauty, Avoskin, N.Pure, and SkinGame) were retrieved via web scraping and automatically labeled into positive and negative classes using a lexicon-based approach. The evaluation results demonstrate that the fine-tuned IndoBERT model achieved an accuracy of 87% under the 80:20 data-splitting scenario and 86% under the 75:25 scenario. In the 75:25 scenario, the model attained a precision of 84%, recall of 94%, and F1-score of 89%, while remaining free from overfitting as shown by the loss curve. In conclusion, the IndoBERT architecture demonstrates high reliability in classifying consumer sentiments despite challenges from slang, dermatological terms, and code-mixing.

Keywords:
Sentiment Analysis IndoBERT Skincare Social Media Natural Language Processing
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Author Biographies
Ratna Juwita Universitas Bina Darma

Program Studi Teknik Informatika, Fakultas Sains dan Teknologi, Universitas Bina Darma, Kota Palembang, Provinsi Sumatera Selatan, Indonesia

Heri Suroyo Universitas Bina Darma

Program Studi Teknik Informatika, Fakultas Sains dan Teknologi, Universitas Bina Darma, Kota Palembang, Provinsi Sumatera Selatan, Indonesia

How to Cite
Juwita, R., & Suroyo, H. (2026). Analisis Sentimen Teks Komentar Produk Skincare Menggunakan Model IndoBERT. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 7(3), 804-812. https://doi.org/10.63447/jimik.v7i3.2098
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This work is licensed under a Copyright (c) 2026 Ratna Juwita, Heri Suroyo .

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References
Total: 14 References
  1. Al Aufar, A. P., & Romadhony, A. (2025). Aspect-based sentiment analysis on beauty product reviews using BERT and Long Short-Term Memory. Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), 14(2), 364–373. https://doi.org/10.23887/janapati.v14i2.94392
  2. Al-Kadzim, M. G., Rasim, R., & Herbert, H. (2025). Analisis perubahan sentimen publik di media sosial X terhadap konflik Palestina-Israel menggunakan model IndoBERT. Digital Transformation Technology, 4(2), 1167–1174. https://doi.org/10.47709/digitech.v4i2.5312
  3. Anggraeni, M. D., Yudiananta, A. C., Arifin, A. H., Arifin, W., Mahfudz, I., & Fauzi, Y. T. (2025). Analisis sentimen masyarakat terhadap permasalahan keracunan program Makan Bergizi Gratis (MBG) pada sosial media 'X'. Jurnal Transformasi (Informasi & Pengembangan Iptek), 21(2), 77–89.
  4. Aras, S., Yusuf, M., Ruimassa, R. Y., Wambrauw, E. A. B., & Pala'langan, E. B. (2024). Sentiment analysis on Shopee product reviews using IndoBERT. Journal of Information Systems and Informatics, 6(3), 1616–1627. https://doi.org/10.51519/journalisi.v6i3.814
  5. Fayola, A., & Darianty, R. (2025). Analisis Sentimen Ulasan Produk Kecantikan di Tokopedia Menggunakan IndoBERT. Information Systems and Applications Journal, 1(1), 11-18.
  6. Fadilah, G. T., Muflikhah, L., & Perdana, R. S. (2025). Analisis sentimen produk hijab pada e-commerce Tokopedia menggunakan algoritma support vector machine dan IndoBERT embedding. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 9(2).
  7. Fauzi, D. A., & Nurajijah, P. R. (2026). Analisis sentimen masyarakat terhadap aplikasi digital Korlantas Polri pada ulasan Google Play Store menggunakan model IndoBERT. Jurnal Pendidikan Sosial dan Humaniora, 5(1), 1181-1204.
  8. Habibi, F. M., Guntara, R. G., & Nuryadin, A. (2025). Komparasi Model IndoBERT dan IndoGPT untuk Analisis Sentimen pada Produk E-commerce. Jurnal Algoritma, 22(2), 2249-2259. https://doi.org/10.33364/algoritma/v.22-2.3070
  9. Hidayat, W. A., & Nastiti, V. R. S. (2024). Perbandingan kinerja pre-trained IndoBERT-Base dan IndoBERT-Lite pada klasifikasi sentimen ulasan TikTok Tokopedia Seller Center dengan model IndoBERT. JSiI (Jurnal Sistem Informasi), 11(2), 13–20. https://doi.org/10.30656/jsii.v11i2.9168
  10. Hingis, K., Karolina, U., & Pribadi, R. (2025). Sentimen analisis overclaim skincare Skintific menggunakan algoritma BERT. Applied Information Technology and Computer Science (AICOMS), 3(2), 1–5. https://doi.org/10.58466/aicoms.v3i2.1715
  11. Kamal, W. W. (2021). Analisis sentimen ulasan produk skincare menggunakan metode Support Vector Machine (Studi kasus: Forum Female Daily) [Skripsi sarjana, nama universitas/institusi]. Repositori Institusi.
  12. Lotfi, C., Srinivasan, S., Ertz, M., & Latrous, I. (2021, January). Web scraping techniques and applications: A literature review. In SCRS conference proceedings on intelligent systems (pp. 381-394). New Delhi, India: SCRS.
  13. Pangestu, A., & Maulana, F. (2026, January). Implementasi Analisis Sentimen Komentar YouTube Berbasis IndoBERT dengan Evaluasi Confidence Score. In Seminar Nasional Teknologi & Sains (Vol. 5, No. 1, pp. 664-672). https://doi.org/10.29407/vvdxca28
  14. Yumna, I., & Rahmatulloh, A. (2026). Sentiment Analysis of Multi-Brand Skincare Product Reviews Using IndoBERT Fine-Tuning. Indonesian Journal of Computing, Engineering, and Design (IJoCED), 8(1), 48-61. https://doi.org/10.35806/ijoced.v8i1.562.
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