JARINGAN SARAF TIRUAN BACKPROPAGATION UNTUK APLIKASI PENGENALAN TANDA TANGAN
| Main Authors: | Widiastuti, Fani, Kaswidjanti, Wilis, Rustamaji, Heru Cahya |
|---|---|
| Format: | Article info application/pdf eJournal |
| Bahasa: | ind |
| Terbitan: |
Jurusan Teknik Informatika
, 2015
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| Subjects: | |
| Online Access: |
http://jurnal.upnyk.ac.id/index.php/telematika/article/view/514 http://jurnal.upnyk.ac.id/index.php/telematika/article/view/514/475 |
Daftar Isi:
- Back propagation neural network is part of a multilayered feedforward neural network (MFN) which has been developed and reliable enough to solve the problem of approximation and pattern classification. Application of artificial neural network (ANN) in pattern recognition is one of the signature pattern recognition. Signature of each person are generally identical but not the same. This means that often a person's signature changes every time. This change concerns the position, size and pressure factors signature. Signature is the most widely used form of identification of a person. In general, to identify the signature is still done manually, by matching signatures at the time of the transaction with a valid signature. Therefore, we need a system that can analyze the characteristic signature making it easier to identify the person's signature. The research methodology used in the development of the system is a method Rappid Guidelines for Application Engineering (GRAPPLE), which only covers the design stage needs (Requirement Gathering), analysis (Analysis), the design (Design), and development (Development). This signature recognition process through several stages. First image through image processing stages, where the image will be used as the image of the gray / grayscaling. Once the image is converted into binary data by using thresholding. After going through the binary image processing, the data obtained will be the input value to the training process by using the backpropagation method. The results of the training will be used for the process of signature recognition. Image signatures used in this study were 80 image signatures from 10 respondents. The ratio between training data and testing data is 5:3. The test results show that the signature is able to recognize applications built with precision signature 84% of the tested signatures. Errors in the identification of signatures occur for several reasons, namely: the position of the signature, the image file is damaged, and the learning process is not maximized.