Published: 2026-07-01

Klasifikasi Kulit Wajah untuk Rekomendasi Produk Skincare Menggunakan Convolutional Neural Network (CNN)

DOI: 10.35870/emt.v10i3.6321

Sri Lestari, Mesra Betty Yel, Ahlan Nur Fallah, Giraldi Freddy Simanungkalit, M. Ilyan Fadiliah, M. Dicky Adicandra, Dadang Iskandar Mulyana, Sutisna Sutisna
  • Sri Lestari: STIKOM Cipta Karya Informatika
  • Mesra Betty Yel: STIKOM Cipta Karya Informatika
  • Ahlan Nur Fallah: STIKOM Cipta Karya Informatika
  • Giraldi Freddy Simanungkalit: STIKOM Cipta Karya Informatika
  • M. Ilyan Fadiliah: STIKOM Cipta Karya Informatika
  • M. Dicky Adicandra: STIKOM Cipta Karya Informatika
  • Dadang Iskandar Mulyana: STIKOM Cipta Karya Informatika
  • Sutisna Sutisna: STIKOM Cipta Karya Informatika
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Abstract

This study utilizes the ResNet50 architecture with transfer learning techniques. The dataset consists of 5,200 facial images categorized into five classes: normal, dry, oily, combination, and sensitive. Data was split with an 80:10:10 ratio for training, validation, and testing. Data augmentation was applied to increase dataset variety. The recommendation system was developed using a content-based filtering approach with rule-based mapping between classification results and product attributes. The ResNet50 model achieved a classification accuracy of 90.4% on test data, with the highest F1-score for the oily class (94.7%) and the lowest for the sensitive class (86.9%). The recommendation system produced a Mean Reciprocal Rank (MRR) of 0.82 and precision@3 of 0.76. User satisfaction testing with 50 participants showed an 84% satisfaction rate. CNN with the ResNet50 architecture is effective for facial skin type classification with high accuracy. The integration of the classification system with content-based recommendation mechanisms successfully provides relevant skincare product recommendations. This system has the potential to become a digital tool that can enhance public skin health literacy and reduce errors in skincare product selection.

Keywords

Facial Skin Classification; Skincare; Convolutional Neural Network (CNN); Resnet50; Recommendation System; Deep Learning; Community Service

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How to Cite

Lestari, S., Yel, M. B., Fallah, A. N., Simanungkalit, G. F., Fadiliah, M. I., Adicandra, M. D., Mulyana, D. I., & Sutisna, S. (2026). Klasifikasi Kulit Wajah untuk Rekomendasi Produk Skincare Menggunakan Convolutional Neural Network (CNN). Jurnal EMT KITA, 10(3), 1377-1382. https://doi.org/10.35870/emt.v10i3.6321
Article Information

This article has been peer-reviewed and published in the Jurnal EMT KITA. The content is available under the terms of the Creative Commons Attribution 4.0 International License.

  • Issue: Vol. 10 No. 3 (2026)

  • Section: Articles

  • Published: 2026-07-01

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Author Biographies
Sri Lestari, STIKOM Cipta Karya Informatika
Google Scholar ResearchGate ORCID

Program Studi Teknik Informatika, Fakultas Ilmu Komputer, STIKOM Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.

Mesra Betty Yel, STIKOM Cipta Karya Informatika
Google Scholar ResearchGate ORCID

Program Studi Teknik Informatika, Fakultas Ilmu Komputer, STIKOM Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.

Ahlan Nur Fallah, STIKOM Cipta Karya Informatika
Google Scholar ResearchGate ORCID

Program Studi Teknik Informatika, Fakultas Ilmu Komputer, STIKOM Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.

Giraldi Freddy Simanungkalit, STIKOM Cipta Karya Informatika
Google Scholar ResearchGate ORCID

Program Studi Teknik Informatika, Fakultas Ilmu Komputer, STIKOM Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.

M. Ilyan Fadiliah, STIKOM Cipta Karya Informatika
Google Scholar ResearchGate ORCID

Program Studi Teknik Informatika, Fakultas Ilmu Komputer, STIKOM Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.

M. Dicky Adicandra, STIKOM Cipta Karya Informatika
Google Scholar ResearchGate ORCID

Program Studi Teknik Informatika, Fakultas Ilmu Komputer, STIKOM Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.

Dadang Iskandar Mulyana, STIKOM Cipta Karya Informatika
Google Scholar ResearchGate ORCID

Program Studi Teknik Informatika, Fakultas Ilmu Komputer, STIKOM Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.

Sutisna Sutisna, STIKOM Cipta Karya Informatika
Google Scholar ResearchGate ORCID

Program Studi Teknik Informatika, Fakultas Ilmu Komputer, STIKOM Cipta Karya Informatika, Kota Jakarta Timur, Daerah Khusus Ibukota Jakarta, Indonesia.

References
  • Aggarwal, C. C. (2016). Recommender systems (Vol. 1, No. 1). Cham: Springer International Publishing.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Baumann, L. (2006). The skin type solution: a revolutionary guide to your best skin ever. Bantam.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Baumann, L. (2008). Understanding and treating various skin types: the Baumann Skin Type Indicator. Dermatologic clinics, 26(3), 359-373.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Draelos, Z. D. (2018). The science behind skin care: Moisturizers. Journal of cosmetic dermatology, 17(2), 138-144. https://doi.org/10.1111/jocd.12490.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. nature, 542(7639), 115-118.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1, No. 2, pp. 1-800). Cambridge: MIT press.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., ... Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv Preprint arXiv:1704.04861.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Misery, L., Boussetta, S., Nocera, T., Perez‐Cullell, N., & Taieb, C. (2009). Sensitive skin in Europe. Journal of the European Academy of Dermatology and Venereology, 23(4), 376-381. https://doi.org/10.1111/j.1468-3083.2008.03037.x.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Mukhopadhyay, P. (2011). Cleansers and their role in various dermatological disorders. Indian journal of dermatology, 56(1), 2-6.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Nelson, K., Taylor, E., & Walsh, D. (2020). Digital health literacy and accessibility. Journal of Medical Internet Research, 22(8), e18456.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Park, S., Lee, J., & Kim, H. (2021). Deep learning-based skin condition classification using facial images. IEEE Access, 9, 43271–43280.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Park, S., Moon, J., & Kim, Y. (2022). Accessibility and usability of AI-based skin diagnosis applications for general public. International Journal of Human-Computer Interaction, 38(5), 412–425.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Research and Markets. (2023). Global skin care products market size, share & trends analysis report 2023–2027.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Ricci, F., Rokach, L., & Shapira, B. (2022). Recommender systems handbook (3rd ed.). Springer.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1), 1–48.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv Preprint arXiv:1409.1556.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
  • World Health Organization. (2021). Digital health for all: Strengthening digital health implementation. WHO Press.
    Google Scholar Scite Semantic Scholar Scilit Crossref Connected Papers
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