KLASIFIKASI PENYAKIT KULIT WAJAH MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK EFFICIENTNET-B3:

Riko Angga Bayu Kusuma; Bambang Irawan; Abdul Khamid

doi:10.23960/jitet.v14i1.8721

Authors

Riko Angga Bayu Kusuma Universitas Muhadi Setiabudi
Bambang Irawan Universitas Muhadi Setiabudi
Abdul Khamid Universitas Muhadi Setiabudi

DOI:

https://doi.org/10.23960/jitet.v14i1.8721

Abstract Views: 90 File Views: 45

Keywords:

Klasifikasi penyakit kulit wajah, Convolutional Neural Network, EfficientNet-B3, Pengolahan citra, Fine-tuning

Abstract

Facial skin diseases are a common health issue that significantly affect an individual's quality of life. Early detection through image processing is a crucial step for timely treatment. This study applies Convolutional Neural Network with EfficientNet-B3 architecture to classify five types of facial skin diseases, namely acne, actinic keratosis, basal cell carcinoma, eczema, and rosacea. The model was developed through fine-tuning on an augmented image dataset, with training and testing data splits. Evaluation results show a testing accuracy of 96.61 percent, accompanied by average precision, recall, and F1-score values of 0.97. The confusion matrix indicates high classification performance with minimal errors between classes. This approach proves effective in improving detection accuracy, thus potentially supporting medical personnel in early diagnosis.

Downloads

Download data is not yet available.

References

N. I. Khani and S. Rakasiwi, “Penerapan Convolutional Neural Network dengan ResNet-50 untuk Klasifikasi Penyakit Kulit Wajah Efektif,” Edumatic: Jurnal Pendidikan Informatika, vol. 9, no. 1, pp. 217–225, 2025.

D. A. Wijaya, A. Triayudi, and A. Gunawan, “Penerapan Artificial Intelligence untuk Klasifikasi Penyakit Kulit dengan Metode CNN,” Journal of Computer System and Informatics, vol. 4, no. 3, pp. 685–692, 2023.

S. N. Ria, M. Walid, and B. A. Umam, “Pengolahan Citra Digital untuk Identifikasi Penyakit Kulit Menggunakan CNN,” Energy: Jurnal Ilmiah Ilmu Teknik, vol. 12, no. 2, pp. 9–16, 2022.

Supirman, C. Lubis, D. Yuliarto, and N. J. Perdana, “Klasifikasi Penyakit Kulit Menggunakan Convolutional Neural Network dengan Arsitektur VGG16,” Simtek: Jurnal Sistem Informasi dan Teknik Komputer, vol. 8, no. 1, pp. 135–140, 2023.

Pangestu and Kusrini, “Peningkatan Kinerja Arsitektur ResNet-50 untuk Menangani Overfitting dalam Klasifikasi Penyakit Kulit,” Tematik, vol. 11, no. 1, pp. 65–71, 2024.

R. A. Mas’ud and J. Zeniarja, “Optimasi CNN untuk Deteksi Citra Medis Menggunakan Global Average Pooling,” Edumatic: Jurnal Pendidikan Informatika, vol. 8, no. 1, pp. 310–318, 2024.

N. B. Pamungkas and A. Suhendar, “Penerapan Transfer Learning pada CNN untuk Klasifikasi Citra,” Edumatic: Jurnal Pendidikan Informatika, vol. 8, no. 2, pp. 675–684, 2024.

W. D. James, T. G. Berger, and D. M. Elston, Andrews’ Diseases of the Skin: Clinical Dermatology. Elsevier, 2016.

H. Kittler, H. Pehamberger, K. Wolff, and M. Binder, “Diagnostic accuracy of dermoscopy,” Lancet Oncol, 2002.

R. C. Gonzalez and R. E. Woods, Digital Image Processing. Pearson Education, 2018.

C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” J Big Data, 2019.

A. S. Lundervold and A. Lundervold, “An overview of deep learning in medical imaging focusing on MRI,” Z Med Phys, 2019.

A. Hosny, C. Parmar, J. Quackenbush, L. H. Schwartz, and H. J. W. L. Aerts, “Artificial intelligence in radiology,” Nat Rev Cancer, 2018.

V. Cheplygina, M. de Bruijne, and J. P. W. Pluim, “Not-so-supervised: a survey of semi-supervised, weakly supervised and transfer learning in medical image analysis,” Med Image Anal, 2019.

M. Tan and Q. V Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” ICML, 2019.

A. Herlangga, R. R. Karim, and M. K. Nurwijaya, “Penerapan Transfer Learning EfficientNetB3 untuk Pengenalan Senjata Tradisional Sumatera Barat Menggunakan CNN,” JITET, 2024.

L. Perez and J. Wang, “The Effectiveness of Data Augmentation in Image Classification using Deep Learning,” arXiv preprint, 2017.

N. Tajbakhsh, J. Y. Shin, and S. R. Gurudu, “Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?,” IEEE Trans Med Imaging, 2016.