Sentiment Analysis of Multi-Brand Skincare Product Reviews Using IndoBERT Fine-Tuning
DOI:
https://doi.org/10.35806/ijoced.v8i1.562Keywords:
Code-mixed, Consumer Opinion, Deep Learning, Digital Platform, Female Daily, Transfer LearningAbstract
The Female Daily platform predominantly features consumer reviews characterized by informal Indonesian and English code-mixing. This specific linguistic complexity presents a significant impediment to traditional classification methods, such as classical machine learning algorithms (e.g., Naive Bayes and SVM) and lexicon-based approaches, which often fail to accurately capture semantic nuances and contextual dependencies in unstructured text. To fill this research gap, this study uses a deep learning method with fine-tuned IndoBERT for multi-brand sentiment analysis. Using a dataset of 12,418 reviews across five popular skincare brands, the model achieved an accuracy of 85%, an F1-score of 0.84, a precision of 0.85, and a recall of 0.84. A key contribution of this research is the multi-brand analysis, which reveals distinct consumer perception patterns: Wardah and Emina achieved the highest proportions of positive sentiment, while Skintific and Garnier demonstrated a more balanced distribution between positive and negative reviews. In contrast, MS Glow exhibited more varied and diverse consumer opinions. These findings confirm that IndoBERT’s self-attention mechanism is highly effective and adaptive in processing the informal, code-mixed vocabulary of the beauty community, outperforming traditional methodologies in both robustness and contextual understanding.
References
Abdullah, A. A., Hassan, M. M., & Mustafa, Y. T. (2024). Leveraging Bayesian deep learning and ensemble methods for uncertainty quantification in image classification: A ranking-based approach. Heliyon, 10(2). https://doi.org/10.1016/j.heliyon.2024.e24188
Anandia, N., Purnomo, W., & Setiawan, N. Y. (2025). Product monitoring terhadap Emina Sun Battle SPF 30 PA+++ pada Sociolla berdasarkan hasil analisis sentimen. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 9(4), 2548–2964.
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
Ayu Puspita, N., Anggraeny, F. T., & Rizki, A. M. (2024). Analisis sentimen dengan algoritma naïve Bayes classifier terhadap ulasan aplikasi My F&B ID. Jurnal Mahasiswa Teknik Informatika.
Dheanis, K., Salsabila, A., & Trianasari, N. (2021). Analisis persepsi produk kosmetik menggunakan metode sentiment analysis dan topic modeling (Studi kasus: Laneige Water Sleeping Mask). Jurnal Teknologi dan Manajemen Informatika, 7(1).
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), 2548–2964.
Habibi, M., & Kusumaningtyas, K. (2023). Customer experience analysis of skincare products through social media data using topic modeling and sentiment analysis. Journal of Science and Applied Engineering, 6(1), 1. https://doi.org/10.31328/jsae.v6i1.4169
Hidayat, E. Y., & Handayani, D. (2023). Penerapan 1D-CNN untuk analisis sentimen ulasan produk kosmetik berdasarkan Female Daily review. Jurnal Nasional Teknologi dan Sistem Informasi, 8(3), 153–163. https://doi.org/10.25077/teknosi.v8i3.2022.153-163
Huang, H., Zavareh, A. A., & Mustafa, M. B. (2023). Sentiment analysis in e-commerce platforms: A review of current techniques and future directions. IEEE Access, 11, 90367–90382. https://doi.org/10.1109/ACCESS.2023.3307308
Huang, J. (2023). Practical application of platform comment sentiment binary classification based on deep learning. Academic Journal of Science and Technology.
Imaduddin, H., A’la, F. Y., & Nugroho, Y. S. (2023). Sentiment analysis in Indonesian healthcare applications using IndoBERT approach. International Journal of Advanced Computer Science and Applications (IJACSA).
Jasmarizal, J., Rahmaddeni, R., Junadhi, J., & Anam, M. K. (2024). Penerapan metode support vector machine untuk analisis sentimen. Indonesian Journal of Computer Science, 13(1).
Jayadianti, H., Kaswidjanti, W., Utomo, A. T., Saifullah, S., Dwiyanto, F. A., & Drezewski, R. (2022). Sentiment analysis of Indonesian reviews using fine-tuning IndoBERT and R-CNN. ILKOM Jurnal Ilmiah, 14(3), 348–354. https://doi.org/10.33096/ilkom.v14i3.1505.348-354
Manoppo, M. R., Kolang, I. C., Fiat, D. N. N., Mawara, R. M. C., Sumarno, A. D. P., Yusupa, A., & Tarigan, V. (2025). Analisis sentimen publik di media sosial terhadap kenaikan PPN 12% di Indonesia menggunakan IndoBERT. Jurnal Kecerdasan Buatan dan Teknologi Informasi, 4(2), 152–163. https://doi.org/10.69916/jkbti.v4i2.322
Mei, N. C., Tiun, S., & Sastria, G. (2024). Multi-label aspect-sentiment classification on Indonesian cosmetic product reviews with IndoBERT model. International Journal of Advanced Computer Science and Applications (IJACSA).
Mughal, N., Mujtaba, G., Shaikh, S., Kumar, A., & Daudpota, S. M. (2024). Comparative analysis of deep natural networks and large language models for aspect-based sentiment analysis. IEEE Access, 12, 60943–60959. https://doi.org/10.1109/ACCESS.2024.3386969
Mutinda, J., Mwangi, W., & Okeyo, G. (2023). Sentiment analysis of text reviews using lexicon-enhanced BERT embedding (LeBERT) model with convolutional neural network. Applied Sciences, 13(3). https://doi.org/10.3390/app13031445
Ounacer, S., Mhamdi, D., Ardchir, S., Daif, A., & Azzouazi, M. (2023). Customer sentiment analysis in hotel reviews through natural language processing techniques. International Journal of Advanced Computer Science and Applications (IJACSA).
Patwardhan, N., Marrone, S., & Sansone, C. (2023). Transformers in the real world: A survey on NLP applications. Information, 14(4). https://doi.org/10.3390/info14040242
Prottasha, N. J., Sami, A. A., Kowsher, M., Murad, S. A., Bairagi, A. K., Masud, M., & Baz, M. (2022). Transfer learning for sentiment analysis using BERT-based supervised fine-tuning. Sensors, 22(11). https://doi.org/10.3390/s22114157
Singh, D., Barve, S. S., & Dwivedi, A. K. (2025). OptiASAR: Optimized aspect-based sentiment analysis of reviews with BiLSTM-GRU and NER-BERT in healthcare decision-making. IEEE Access, 13, 47459–47473. https://doi.org/10.1109/ACCESS.2025.3549303
Widya Astuti, L., & Sari, Y. (2023). Code-mixed sentiment analysis using transformer for Twitter social media data. International Journal of Advanced Computer Science and Applications (IJACSA).
Wildana, A., & Kautsar, A. (2025). Sistem rekomendasi pemilihan produk skincare. Jurnal Global Ilmiah, 2(5).
Yutika, C. H., Adiwijaya, A., & Faraby, S. A. (2021). Analisis sentimen berbasis aspek pada review Female Daily menggunakan TF-IDF dan naïve Bayes. Jurnal Media Informatika Budidarma, 5(2), 422. https://doi.org/10.30865/mib.v5i2.2845
