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All Journal Energy : Jurnal Ilmiah Ilmu-Ilmu Teknik
Busro Akramul Umam
Program Teknologi informatika, Fakultas Teknik, Universitas Islam
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Pengolahan Citra Digital Untuk Identifikasi Jenis Penyakit Kulit Menggunakan Metode Convolutional Neural Network (CNN) Sona Nova Ria; Miftahul Walid; Busro Akramul Umam
Energy - Jurnal Ilmiah Ilmu-Ilmu Teknik Vol 12 No 2 (2022): Jurnal Energy Vol. 12 No. 2 November 2022
Publisher : Fakultas Teknik

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.v12i2.1118

Abstract

Skin disease is the most common disease and the fastest to infect the human body. This happens because the skin is the first organ to receive external stimuli in the form of touch, temperature and other stimuli. Skin diseases consist of several types that have almost the same color and texture with the naked eye. Thus, an approach is needed to identify the type of skin disease with the help of an image processing system, and an artificial neural network. The identification method used in this research is Convolutional Neural Network (CNN). The infected skin image is used as an input image for image processing. Before being identified, image preprocessing is done, namely resizing, grayscalling, using the Convolutional Neural Network method. The testing process in this study uses 70 types of skin disease images, validation data and 35 types of skin disease images for testing data. The results of this study are the Convolutional Neural Network method can recognize each type of skin disease image with an accuracy of 98% in the validation testing process and 85% in the testing process. Keywords : Skin disease, Convolutional Neural Network, Digital Image Processing
Co-Authors Sona Nova Ria Walid, Miftahul