Implementation of Deep Learning on Number Recognition in Sign Language

Sisfotenika

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Field Value
 
Title Implementation of Deep Learning on Number Recognition in Sign Language
 
Creator Celsia, Fini Keni; Fakultas Ilmu Komputer, Universitas Klabat
Sandag, Green Arther; Universitas Klabat
 
Subject Digital planimetry; Image processing; HSV; Diabetic wound; Contour image;
 
Description Measuring the wound area in diabetics is still using a manual way with a wound ruler. Whereas the ruler affixed to the wound will become a contaminated agent that can transmit the infection to other recipients. Digital measurement methods are needed to solve the problem. However, clarifying the boundaries between the wound and the skin requires carefulness and high accuracy. For this reason, it has needed an imaging method that can do segmentation between the wound and the skin boundary for diabetic patients based on digital, called digital planimetry. This study uses a masking contour image processing algorithm from the Hue, Saturation, Value (HSV), Then doing iteration five times and gamma filter. So the result of segmentation is formed. This study concludes that the segmentation with this method has not been able to perform the segment properly, and it requires more masking values, but the results of the 5th iteration got a minor error, which is 0.002%. The digital imaging carried out in this study could be developed to be a digital-based diabetic patient wound measurement tool.
 
Publisher STMIK PONTIANAK
 
Contributor Fakultas Ilmu Komputer, Universitas Klabat
 
Date 2021-04-22
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier http://sisfotenika.stmikpontianak.ac.id/index.php/ST/article/view/1117
10.30700/jst.v11i2.1117
 
Source SISFOTENIKA; Vol 11, No 2 (2021): SISFOTENIKA; 124-136
SISFOTENIKA; Vol 11, No 2 (2021): SISFOTENIKA; 124-136
2460-5344
2087-7897
10.30700/jst.v11i2
 
Language ind
 
Relation http://sisfotenika.stmikpontianak.ac.id/index.php/ST/article/view/1117/747
 
Rights Copyright (c) 2021 SISFOTENIKA
http://creativecommons.org/licenses/by/4.0
 

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