Deteksi Otomatis Penyakit Kuku Menggunakan Deep Learning Berbasis CNN
DOI:
https://doi.org/10.29407/9cadwf02Abstract
Penelitian ini bertujuan mengembangkan model klasifikasi citra kuku untuk mendeteksi kondisi sehat, Koilonychia, dan Onychomycosis menggunakan arsitektur Convolutional Neural Network (CNN) VGG-16. Dataset citra kuku dipra-proses dengan augmentasi khusus (spoon_augmentation) untuk menonjolkan fitur cekungan Koilonychia. Model VGG-16, dengan Fine-Tuning pada blok 5, dilatih pada 1588 citra pelatihan dan dievaluasi pada 286 citra pengujian. Hasil evaluasi menunjukkan akurasi 90.56%, dengan precision 81.36% untuk Koilonychia, 97.47% untuk kuku sehat, dan 90.54% untuk Onychomycosis. Sistem ini terbukti andal dalam mendeteksi kelainan kuku, berpotensi mendukung dermatolog dalam diagnosis cepat dan akurat. Pendekatan ini menunjukkan efektivitas Fine-Tuning VGG-16 pada dataset terbatas, menjadikannya solusi potensial untuk aplikasi diagnosis berbasis citra medis.
Keywords:
VGG-16, Kuku, Koilonychia, Klasifikasi, CNN##plugins.themes.default.displayStats.downloads##
References
[1] R. Rusmini, H. Kurniasih, and A. Widiastuti, “Prevalensi Kejadian Penyakit Tidak Menular (PTM),” Jurnal Keperawatan Silampari, vol. 6, no. 2, pp. 1032–1039, Jan. 2023, doi: 10.31539/jks.v6i2.4967.
[2] E. Herdiana, L. Saniah, and F. Reyta, “Deteksi Jenis Penyakit melalui Perubahan Warna Kuku dengan Teknik Image Processing,” Jurnal Accounting Information System (AIMS), vol. 5, no. 1, pp. 81–92, Mar. 2022, doi: 10.32627/aims.v5i1.443.
[3] D. Gunawan and H. Setiawan, “Convolutional Neural Network dalam Citra Medis,” KONSTELASI: Konvergensi Teknologi dan Sistem Informasi, vol. 2, no. 2, May 2022, doi: 10.24002/konstelasi.v2i2.5367.
[4] I. Kurniastuti, A. Andini, and P. Nerisafitra, “Rancang Bangun Program Penentuan Fitur Tekstur Citra Kuku Jari Tangan sebagai Deteksi Dini Resiko Diabetes Mellitus,” Applied Technology and Computing Science Journal, vol. 2, no. 1, pp. 1–9, Sep. 2019, doi: 10.33086/atcsj.v2i1.1138.
[5] N. J. Peksi, B. Yuwono, and M. Y. Florestiyanto, “Classification of Anemia with Digital Images of Nails and Palms using the Naive Bayes Method,” Telematika, vol. 18, no. 1, p. 118, Mar. 2021, doi: 10.31315/telematika.v18i1.4587.
[6] M. O. Nugraha, R. Purnamasari, and S. Aulia, “Klasifikasi Penyakit Berdasarkan Warna Kuku Menggunakan Pengolahan Sinyal Digital,” eProceedings of Engineering, vol. 8, no. 6, pp. 2355–9365, 2022.
[7] M. F. Naufal, “Analisis Perbandingan Algoritma SVM, KNN, dan CNN untuk Klasifikasi Citra Cuaca,” Jurnal Teknologi Informasi dan Ilmu Komputer, vol. 8, no. 2, pp. 311–318, Mar. 2021, doi: 10.25126/jtiik.2021824553.
[8] F. N. Cahya, N. Hardi, and D. Riana, “Klasifikasi penyakit mata menggunakan convolutional neural network (CNN),” SISTEMASI, vol. 10, no. 3, pp. 618–626, 2021.
[9] M. A. Hanin, R. Patmasari, and R. Y. N. Fuâ, “Sistem Klasifikasi Penyakit Kulit Menggunakan Convolutional Neural Network (CNN),” eProceedings of Engineering, vol. 8, no. 1, pp. 273–281, Feb. 2021.
[10] R. Ardianto, D. Yusuf, R. B. B. Sumantri, D. Febrina, R. R. Al-Hakim, and A. S. S. Ariyanto, “Bioinformatics-driven deep learning for nail disease diagnosis: A novel approach to improve healthcare outcomes,” BIO Web Conf, vol. 152, p. 01024, Jan. 2025, doi: 10.1051/bioconf/202515201024.
[11] F. Ramadhani, A. Satria, and S. Salamah, “Implementasi Algoritma Convolutional Neural Network dalam Mengidentifikasi Dini Penyakit pada Mata Katarak,” sudo Jurnal Teknik Informatika, vol. 2, no. 4, pp. 167–175, Dec. 2023, doi: 10.56211/sudo.v2i4.408.
[12] M. R. Alwanda, R. P. K. Ramadhan, and D. Alamsyah, “Implementasi Metode Convolutional Neural Network Menggunakan Arsitektur LeNet-5 untuk Pengenalan Doodle,” Jurnal Algoritme, vol. 1, no. 1, pp. 45–56, Oct. 2020, doi: 10.35957/algoritme.v1i1.434.
[13] K. Vignesh, G. Yadav, and A. Sethi, “Abnormal Event Detection on BMTT-PETS 2017 Surveillance Challenge,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE, Jul. 2017, pp. 2161–2168. doi: 10.1109/CVPRW.2017.268.
[14] S. Tammina, “Transfer learning using VGG-16 with Deep Convolutional Neural Network for Classifying Images,” International Journal of Scientific and Research Publications (IJSRP), vol. 9, no. 10, p. p9420, Oct. 2019, doi: 10.29322/IJSRP.9.10.2019.p9420.
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