sistem pengenalan karakter tulisan tangan latin dengan menggunakan cell matriks pada metode supervised learning=Latin handwritten character recognition system using matrix cells ...

When computer technology progression was reached in 20th century, many new branches of computer science developed based on this computer technology development. Artificial intelligent is one of many new branches that developed. Artificial neural network is one of many branches of artificial intellig...

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Main Author: Perpustakaan UGM, i-lib
Format: Article
Published: [Yogyakarta] : Universitas Gadjah Mada 2004
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author Perpustakaan UGM, i-lib
author_facet Perpustakaan UGM, i-lib
author_sort Perpustakaan UGM, i-lib
collection UGM
description When computer technology progression was reached in 20th century, many new branches of computer science developed based on this computer technology development. Artificial intelligent is one of many new branches that developed. Artificial neural network is one of many branches of artificial intelligent that investigate human brain neural in computer, it is hoped that computer can do human tasks, such as classification, assoiation, optimization and self organizing. Neural network built is only the replica of human brain neural network, which is limited and impossible to compete with human brain. But then, the problem lays on how the system can be built so it can do a good job. This research is to develop the ANN system to recognize human's handwritten characters with various deformation. The system makes use of the 25 x 20 pixel matrix cell to represent a single character. This matrix cell is grouped into 5 x 5 pixel submatrix, resulting another 5 x 4 new matrix. The element of the last matrix is the number which representing the number of pixels covered by the character being recognized. The 5 x 4 matrix then is multiplied with (0 1 1 0) vector, which resulting a vector as an input of the ANN. The ANN recognizing algorithm used here is the back-propagation combined with supervised learning method. The rate of learning is given 0.05 with error tolerance 0.01. The experimental results show that the system performs quite well with 86,9% average accuracy. Keywords : artificial neural network, back-propagation, matrix cell, supervised learning, target error
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spelling oai:generic.eprints.org:176542014-06-18T00:29:34Z https://repository.ugm.ac.id/17654/ sistem pengenalan karakter tulisan tangan latin dengan menggunakan cell matriks pada metode supervised learning=Latin handwritten character recognition system using matrix cells ... Perpustakaan UGM, i-lib Jurnal i-lib UGM When computer technology progression was reached in 20th century, many new branches of computer science developed based on this computer technology development. Artificial intelligent is one of many new branches that developed. Artificial neural network is one of many branches of artificial intelligent that investigate human brain neural in computer, it is hoped that computer can do human tasks, such as classification, assoiation, optimization and self organizing. Neural network built is only the replica of human brain neural network, which is limited and impossible to compete with human brain. But then, the problem lays on how the system can be built so it can do a good job. This research is to develop the ANN system to recognize human's handwritten characters with various deformation. The system makes use of the 25 x 20 pixel matrix cell to represent a single character. This matrix cell is grouped into 5 x 5 pixel submatrix, resulting another 5 x 4 new matrix. The element of the last matrix is the number which representing the number of pixels covered by the character being recognized. The 5 x 4 matrix then is multiplied with (0 1 1 0) vector, which resulting a vector as an input of the ANN. The ANN recognizing algorithm used here is the back-propagation combined with supervised learning method. The rate of learning is given 0.05 with error tolerance 0.01. The experimental results show that the system performs quite well with 86,9% average accuracy. Keywords : artificial neural network, back-propagation, matrix cell, supervised learning, target error [Yogyakarta] : Universitas Gadjah Mada 2004 Article NonPeerReviewed Perpustakaan UGM, i-lib (2004) sistem pengenalan karakter tulisan tangan latin dengan menggunakan cell matriks pada metode supervised learning=Latin handwritten character recognition system using matrix cells ... Jurnal i-lib UGM. http://i-lib.ugm.ac.id/jurnal/download.php?dataId=415
spellingShingle Jurnal i-lib UGM
Perpustakaan UGM, i-lib
sistem pengenalan karakter tulisan tangan latin dengan menggunakan cell matriks pada metode supervised learning=Latin handwritten character recognition system using matrix cells ...
title sistem pengenalan karakter tulisan tangan latin dengan menggunakan cell matriks pada metode supervised learning=Latin handwritten character recognition system using matrix cells ...
title_full sistem pengenalan karakter tulisan tangan latin dengan menggunakan cell matriks pada metode supervised learning=Latin handwritten character recognition system using matrix cells ...
title_fullStr sistem pengenalan karakter tulisan tangan latin dengan menggunakan cell matriks pada metode supervised learning=Latin handwritten character recognition system using matrix cells ...
title_full_unstemmed sistem pengenalan karakter tulisan tangan latin dengan menggunakan cell matriks pada metode supervised learning=Latin handwritten character recognition system using matrix cells ...
title_short sistem pengenalan karakter tulisan tangan latin dengan menggunakan cell matriks pada metode supervised learning=Latin handwritten character recognition system using matrix cells ...
title_sort sistem pengenalan karakter tulisan tangan latin dengan menggunakan cell matriks pada metode supervised learning latin handwritten character recognition system using matrix cells
topic Jurnal i-lib UGM
work_keys_str_mv AT perpustakaanugmilib sistempengenalankaraktertulisantanganlatindenganmenggunakancellmatrikspadametodesupervisedlearninglatinhandwrittencharacterrecognitionsystemusingmatrixcells