PERBANDINGAN VERIFIKASI TANDA TANGAN DENGAN MENGGUNAKAN JARINGAN SARAF TIRUAN BACKPROPAGATION DAN SUPPORT VECTOR MACHINE
Signature represents biometric feature useful to verify individual�s identity. The study presents the implementation of the signature identification with Support Vector Machine and compared with neural network backpropagation of 100 signature samples. Feature extraction using vertical splitting an...
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| Main Authors: | , |
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| 格式: | Theses and Dissertations NonPeerReviewed |
| 出版: |
[Yogyakarta] : Universitas Gadjah Mada
2014
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| 主題: | |
| 在線閱讀: | https://repository.ugm.ac.id/130236/ http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=70651 |
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| 總結: | Signature represents biometric feature useful to verify individualâ��s
identity. The study presents the implementation of the signature identification with
Support Vector Machine and compared with neural network backpropagation of
100 signature samples. Feature extraction using vertical splitting and horizontal
splitting to get the value of the angle and distance as the characteristic value of
the signature which would then be in the process. Artificial neural network
classification methods Backpropagation earning Neural Network (ANN-BP) and
support vector machine (SVM). The Artificial Neural Network with
Backpropagation learning method consists of 100 input nodes, 3 hidden layer
and 2 output nodes, while its learning function uses resilient backpropagation.
Support Vector Machine (SVM) is implemented using one-againt-one method.
ANN-BP has an accuracy in the verification of 98.5%, while SVM 94.5% for
Traced signatures, ANN with back propagation method is able to verify with the
accuracy of 82%, while for the new SVM signatures could not be verified with an
accuracy of 43%. The JST with the backpropagation method is faster for the
learning process than the SVM. |
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