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All Journal Energy: Jurnal Ilmiah Ilmu-ilmu Teknik
Ria, Sona Nova
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Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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Digital Image Processing for Identification of Types of Skin Diseases Using the Convolutional Neural Network (CNN) Method Ria, Sona Nova; Walid , Miftahul; Umam, Busro Akramul
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK Vol. 12 No. 2 (2022): ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.v12i2.p62-67

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 stimulation from the outside in the form of touch, temperature and other stimuli. Skin disease  consists  of  several  types  that  have  a  color  texture  that  is  almost  the  same  by  naked  eye.  Thus,  an approach  is  needed  to  recognize  the  types  of  skin  diseases  with  the  help  of  image  processing  systems  and artificial  neural  networks.  The  identification  method  used  in  this  study  is  the  Convolutional  Neural  Network (CNN).  The  infected  skin  image  is  used  as  an  input  image for  image processing.  Prior  to identification,  image pre-processing was carried out, namely resizing, grayscaling, using the Convolutional Neural Network method. The  testing  process  in  this  study  used  70  types  of  skin  disease  images  for  validation  data  and  35  types  of  skin disease  images  for  data  testing.  The  results  of  this  study  the  Convolutional  Neural  Network  method  can recognize  each  image  of  a  type  of  skin  disease  with  anaccuracy  of  98%  in  the  validation  testing  process  and 85% in the testing process.
Co-Authors Umam, Busro Akramul Walid , Miftahul