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All Journal Energy: Jurnal Ilmiah Ilmu-ilmu Teknik
Miftahul Walid
Program Studi Teknik Informatika, Fakultas Teknik, Universitas Islam Madura
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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Document Signature Image Recognition System Using the CNN (Convolutional Neural Network) Method Raudlatul Jannah; Miftahul Walid; Hoiriyah
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.p54-61

Abstract

A signature is a marker or identity in a document. Signatures have an essential role in verifying and legalizing documents. The signature is not just any sign but is a legal one and is the original image of the owner. With the development of today's technology, signature pattern identification can not only be done manually but can also be  done  with  the  help  of  a  computer.  However,  computers  cannot  immediately  carry  out  the  identification process, instead, a pattern recognition process is needed beforehand which can be done by extracting signature features.  This  research  aims  to  determine  signature  ownership,  so  identification  is  required.  Identification  of signature  patterns  is  needed  to  recognize  and  distinguish  the  signatures  of  each  individual based  on  the characteristics  of  the  signature.  Therefore  this  research  is  expected  to  be  an  alternative  to  minimize  signature recognition errors using the CNN (convolutional neural network) method. then taken with a scanner. The results of this research are that the Convolutional Neural Network method can recognize each signature image with an accuracy of 100% in the validation testing process and 85% in the testing process.
Co-Authors Hoiriyah Raudlatul Jannah