Implementasi Transfer Learning Menggunakan Convolutional Neural Network untuk Deteksi Jenis Kulit Wajah


  • Karlina Dwi Septiani * Mail Universitas Dian Nuswantoro, Semarang, Indonesia
  • Egia Rosi Subhiyakto Universitas Dian Nuswantoro, Semarang, Indonesia
  • (*) Corresponding Author
Keywords: Facial skin; Transfer learning; CNN; Image classification; CreateML

Abstract

In Indonesia, extreme tropical climate conditions with high humidity and sun exposure increase the risk of facial skin problems for the community. Facial skin that is not properly cared for is often prone to disorders, ranging from dry skin, oily skin, to acne. However, Indonesian people's awareness of the importance of maintaining healthy skin is still relatively low, which is exacerbated by limited time and access to consult a dermatologist. Most people may not know their skin type, even though each skin type requires different care to stay healthy and avoid more serious skin problems. To answer this problem, this study aims to develop an iOS-based application that is able to automatically detect facial skin types using transfer learning with a Convolutional Neural Network (CNN) architecture. The model was developed by training a dataset of facial images to classify skin types such as dry, oily, normal, and acne-prone, and integrated into an iOS application for real-time analysis through user facial images. The evaluation results showed a model accuracy of 87% and an application accuracy of 83.3% in identifying facial skin types. It is hoped that this application will help Indonesian people better understand their skin conditions and obtain appropriate treatment recommendations to maintain healthy skin in a tropical climate.

Downloads

Download data is not yet available.

References

N. Gunarti et al., “Artikel Review: Kandungan Senyawa Aktif Tanaman Untuk Kesehatan Kulit,” Jurnal Farmasi Indonesia, vol. 14, no. 2, pp. 190–195, Jul. 2022, doi: 10.35617/jfionline.v14i2.86.

Fendy Wellen, S. T. Tan, Yohanes Firmansyah, and Hendsun Hendsun, “Correlation between Facial Skin Damage Due to UV Exposure and Facial Skin Porphyrin Level: Study on Students of SMA Kalam Kudus II Jakarta, Indonesia,” Bioscientia Medicina : Journal of Biomedicine and Translational Research, vol. 6, no. 18, pp. 2948–2952, Feb. 2023, doi: 10.37275/bsm.v6i18.737.

R. Efata, W. I. Loka, N. Wijaya, and D. Suhartono, “Facial Skin Type Prediction Based on Baumann Skin Type Solutions Theory Using Machine Learning,” TEM Journal, vol. 12, no. 1, pp. 96–103, Feb. 2023, doi: 10.18421/TEM121-13.

N. F. Nissa, A. Janiati, N. Cahya, Anton, and P. Astuti, “Application of Deep Learning Using Convolutional Neural Network (CNN) Method For Women’s Skin Classification,” Scientific Journal of Informatics, vol. 8, no. 1, pp. 144–153, May 2021, doi: 10.15294/sji.v8i1.26888.

S. Sunnam and A. Obulesh, “Classification Facial Skin and Treatment Suggestions for Good Skin Using Deep Learning with Region of Interest (ROI) Patches,” International Journal of Engineering Research in Computer Science and Engineering (IJERCSE), vol. 9, no. 9, pp. 2394–2320, Sep. 2022, doi: 10.36647/ijercse/09.09.art013.

H. T. Chan, Y. W. Liao, S. Y. Jhong, S. C. Chien, K. L. Hua, and Y. Y. Chen, “A Skin Type Classification Method Using Mobile Device-Based Deep Learning Model,” in 2023 9th International Conference on Applied System Innovation, ICASI 2023, Institute of Electrical and Electronics Engineers Inc., 2023, pp. 199–201. doi: 10.1109/ICASI57738.2023.10179572.

A. Kothari, D. Shah, T. Soni, and S. Dhage, “Cosmetic Skin Type Classification Using CNN With Product Recommendation,” in 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2021, pp. 1–6. doi: 10.1109/ICCCNT51525.2021.9580174.

H.-T. Chan, Y.-W. Liao, S.-Y. Jhong, S.-C. Chien, K.-L. Hua, and Y.-Y. Chen, “A Skin Type Classification Method Using Mobile Device-Based Deep Learning Model,” in 2023 9th International Conference on Applied System Innovation (ICASI), IEEE, Apr. 2023, pp. 199–201. doi: 10.1109/ICASI57738.2023.10179572.

I. Ahmad, A. T. Prastowo, E. Suwarni, and I. Borman, “PENGEMBANGAN APLIKASI ONLINE DELIVERY SEBAGAI UPAYA UNTUK MEMBANTU PENINGKATAN PENDAPATAN,” JMM (Jurnal Masyarakat Mandiri), vol. 5, no. 6, pp. 3033–3044, 2021, doi: 10.31764/jmm.v5i6.5413.

Mansur, S. Alam, and M. Ihsar, “APLIKASI SISTEM INFORMASI GEOGRAFIS (SIG) PEMETAAN LAHAN PERTANIAN DAN KOMODITAS HASIL PANEN DI KABUPATEN SIDRAP BERBASIS WEB,” JURNAL SINTAKS LOGIKA, vol. 2, no. 1, Jan. 2022, doi: 10.31850/jsilog.v2i1.

I. D. Susanti, S. Winarno, and J. Zeniarja, “Yogyakarta Batik Image Classification Based on Convolutional Neural Network,” Advance Sustainable Science, Engineering and Technology, vol. 6, no. 1, Jan. 2024, doi: 10.26877/asset.v6i1.18002.

G. Henry, A. Panjaitan, and F. Simatupang, “Pemodelan Klasifikasi Penyakit Daun Tanaman Tomat dengan Convolutional Neural Network Algorithm,” KLIK: Kajian Ilmiah Informatika dan Komputer, vol. 4, no. 5, pp. 2667–2675, Apr. 2024, doi: 10.30865/klik.v4i5.1646.

R. Archana and P. S. E. Jeevaraj, “Deep learning models for digital image processing: a review,” Artif Intell Rev, vol. 57, no. 1, Jan. 2024, doi: 10.1007/s10462-023-10631-z.

K. Tantayakul and W. Panichpattanakul, “A Comparative Study of Machine Learning for iOS Based on Siam Betta Mobile Application,” IEEE, pp. 104–109, 2020, doi: 10.1109/incit50588.2020.9310932.

D. Kasperek, M. Podpora, and A. Kawala-Sterniuk, “Comparison of the Usability of Apple M1 Processors for Various Machine Learning Tasks,” Sensors, vol. 22, no. 20, Oct. 2022, doi: 10.3390/s22208005.

A. Lamia and A. Fawaz, “Detection of Pneumonia Infection by Using Deep Learning on a Mobile Platform,” Hindawi Computational Intelligence and Neuroscience, vol. 2022, 2022, doi: 10.1155/2022/7925668.

R. Aryanto, M. Alfan Rosid, and S. Busono, “Penerapan Deep Learning untuk Pengenalan Tulisan Tangan Bahasa Aksara Lota Ende dengan Menggunakan Metode Convolutional Neural Networks,” Jurnal Informasi dan Teknologi, pp. 258–264, May 2023, doi: 10.37034/jidt.v5i1.313.

Y. K. Bintang, H. Imaduddin, and Y. Kasnanda, “PENGEMBANGAN MODEL DEEP LEARNING UNTUK DETEKSI RETINOPATI DIABETIK MENGGUNAKAN METODE TRANSFER LEARNING,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 9, no. 3, pp. 1442–1455, 2024, doi: 10.29100/jipi.v9i3.5588.

J. Liu, H. Sun, and J. Katto, “Learned Image Compression with Mixed Transformer-CNN Architectures,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, doi: 10.1109/CVPR52729.2023.01383.

B. H. Rambe, R. Pane, D. Irmayani, M. Nasution, and I. R. Munthe, “UML Modeling and Black Box Testing Methods in the School Payment Information System,” Jurnal Mantik, vol. 4, no. 3, pp. 1634–1640, Nov. 2020, doi: https://doi.org/10.35335/mantik.Vol4.2020.969.pp1634-1640.

M. Heydarian, T. E. Doyle, and R. Samavi, “MLCM: Multi-Label Confusion Matrix,” IEEE Access, vol. 10, pp. 19083–19095, 2022, doi: 10.1109/ACCESS.2022.3151048.

B. Yang, R. Nazari, D. Elmo, D. Stead, and E. Eberhardt, “Data preparation for machine learning in rock engineering,” in IOP Conference Series: Earth and Environmental Science, IOP Publishing Ltd, 2023. doi: 10.1088/1755-1315/1124/1/012072.


Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Implementasi Transfer Learning Menggunakan Convolutional Neural Network untuk Deteksi Jenis Kulit Wajah

Dimensions Badge
Article History
Submitted: 2024-10-30
Published: 2024-12-03
Abstract View: 51 times
PDF Download: 35 times
How to Cite
Septiani, K., & Subhiyakto, E. (2024). Implementasi Transfer Learning Menggunakan Convolutional Neural Network untuk Deteksi Jenis Kulit Wajah. Building of Informatics, Technology and Science (BITS), 6(3), 1499−1508. https://doi.org/10.47065/bits.v6i3.6154
Issue
Section
Articles