Penerapan Artificial Intelligence Untuk Klasifikasi Penyakit Kulit Dengan Metode Convolutional Neural Network Berbasis Web
Abstract
Skin is one of the human organs that functions to regulate body temperature in humans, as well as to protect all organs in the human body. There are many factors that affect skin health conditions that cause skin diseases. A system was developed to help people detect skin diseases. This system is Artificial Intelligence with Convolutional Neural Network method so that it will produce a very significant image. The network will be trained to find angles, edges, shapes, and also features. The results of system performance in this study using adam optimizer with a learning rate of 0.0001 get the highest value of data accuracy reaching a value of 97%. So that the identification of skin diseases is quite good.
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Y. K. Kumarahadi, M. Z. Arifin, S. Pambudi, T. Prabowo, and K. Kusrini, “SISTEM PAKAR IDENTIFIKASI JENIS KULIT WAJAH DENGAN METODE CERTAINTY FACTOR,” Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN), vol. 8, no. 1, Apr. 2020, doi: 10.30646/tikomsin.v8i1.453.
Y. A. Hasma and W. Silfianti, “IMPLEMENTASI DEEP LEARNING MENGGUNAKAN FRAMEWORK TENSORFLOW DENGAN METODE FASTER REGIONAL CONVOLUTIONAL NEURAL NETWORK UNTUK PENDETEKSIAN JERAWAT,” Jurnal Ilmiah Teknologi dan Rekayasa, vol. 23, no. 2, pp. 89–102, 2018, doi: 10.35760/tr.2018.v23i2.2459.
S. N. Ria, M. Walid, and B. A. Umam, “Pengolahan Citra Digital Untuk Identifikasi Jenis Penyakit Kulit Menggunakan Metode Convolutional Neural Network (CNN),” Energy - Jurnal Ilmiah Ilmu-Ilmu Teknik, vol. 12, no. 2, pp. 9–16, Dec. 2022, doi: 10.51747/energy.v12i2.1118.
“Deteksi Jenis Kulit Wajah Menggunakan Convolutional Neural Network Arsitektur Mobilenet Detection Of Facial Skin Type Classification Using Convolutional Neural Network With Mobilenet Architecture.”
I. W. Prastika, E. Zuliarso, J. T. Lomba, J. No, and S. 50241, “DETEKSI PENYAKIT KULIT WAJAH MENGGUNAKAN TENSORFLOW DENGAN METODE CONVOLUTIONAL NEURAL NETWORK,” Jurnal Manajemen informatika & Sistem Informasi), vol. 4, no. 2, 2021, [Online]. Available: http://e-journal.stmiklombok.ac.id/index.php/misi
R. Patmasari and S. Saidah, “KLASIFIKASI JENIS KULIT WAJAH MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK SKIN CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK.”
M. A. Hanin, R. Patmasari, R. Yunendah, and N. Fu’adah, “SISTEM KLASIFIKASI PENYAKIT KULIT MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN) SKIN DISEASE CLASSIFICATION SYSTEM USING CONVOLUTIONAL NEURAL NETWORK (CNN).”
L. Triyono, A. Nur, A. Thohari, I. Hestiningsih, and A. Yobioktabera, “KLASIFIKASI PENYAKIT KULIT MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK.”
X. Shen, J. Zhang, C. Yan, and H. Zhou, “An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network,” Sci Rep, vol. 8, no. 1, Dec. 2018, doi: 10.1038/s41598-018-24204-6.
F. Paraijun et al., “Implementasi Algoritma Convolutional Neural Network Dalam Mengklasifikasi Kesegaran Buah Berdasarkan Citra Buah,” vol. 11, no. 1, 2022, doi: 10.33322/kilat.v11i1.1458.
A. Hibatullah and I. Maliki, “PENERAPAN METODE CONVOLUTIONAL NEURAL NETWORK PADA PENGENALAN POLA CITRA SANDI RUMPUT.”
D. Kurnia, “Identifikasi Obesitas Pada Balita Di Posyandu Berbasis Artificial Intelligence,” Jurnal Sains dan Informatika, vol. 4, no. 1, pp. 76–86, Apr. 2018, doi: 10.22216/jsi.v4i1.3370.
J. Wijaya, S. Putra Sutra, P. Wahyu Kosasih, P. Sirait, and J. SIFO Mikroskil, “Implementasi Convolutional Neural Network Untuk Identifikasi Jenis Tanaman Melalui Daun,” Julyxxxx, vol. 21, pp. 1–5, 2020.
E. Rasywir, R. Sinaga, Y. Pratama, U. Dinamika, and B. Jambi, “Analisis dan Implementasi Diagnosis Penyakit Sawit dengan Metode Convolutional Neural Network (CNN),” vol. 22, no. 2, 2020, doi: 10.31294/p.v21i2.
R. Pakpahan, “ANALISA PENGARUH IMPLEMENTASI ARTIFICIAL INTELLIGENCE DALAM KEHIDUPAN MANUSIA,” Journal of Information System, Informatics and Computing Issue Period, vol. 5, no. 2, pp. 506–513, 2021, doi: 10.52362/jisicom.v5i2.616.
A. D. Aryanto, J. Santoso, and D. D. Purwanto, “SISTEM REKOMENDASI OBAT PENGGANTI MENGGUNAKAN METODE CNN STATUS ARTIKEL Dikirim,” 2021.
R. Mawan, “Klasifikasi motif batik menggunakan convolutional neural network”, doi: 10.36802/jnanaloka.
S. Febrian Tumewu, “Klasifikasi Motif Batik menggunakan metode Deep Convolutional Neural Network dengan Data Augmentation.”
F. M. Qotrunnada and P. H. Utomo, “Metode Convolutional Neural Network untuk Klasifikasi Wajah Bermasker,” Prosiding Seminar Nasional Matematika, vol. 5, pp. 799–807, 2022, [Online]. Available: https://journal.unnes.ac.id/sju/index.php/prisma/
F. Sudana Putra, D. Otomatis Jerawat Wajah, and M. P. Kurniawan, “Deteksi Otomatis Jerawat Wajah Menggunakan Metode Convolutional Neural Network (CNN),” JIFOTECH (JOURNAL OF INFORMATION TECHNOLOGY, vol. 1, no. 2, 2021.
Nurkhasanah and Murinto, “Klasifikasi Penyakit Kulit Wajah Menggunakan Metode Convolutional Neural Network Classification of Facial Skin Diseases Using the Method of the Convolutional Neural Network,” SAINTEKS, vol. 18, no. 2, 2021, [Online]. Available: https://www.kaggle.com/datasets
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