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Pattern Recognition and Classification Using Backpropagation Neural Network Algorithm for Songket Motifs Image Retrieval

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@article{IJASEIT2200,
   author = {- Yuhandri and Sarifuddin Madenda and Eri Prasetyo Wibowo and - Karmilasari},
   title = {Pattern Recognition and Classification Using Backpropagation Neural Network Algorithm for Songket Motifs Image Retrieval},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {6},
   year = {2017},
   pages = {2343--2349},
   keywords = {songket; moore; chain code; CBIR; BPNN; neuron; precision value; recall value},
   abstract = {This paper proposes a method of extraction, classification and pattern recognition songket cloth texture. Features Chain-code pattern texture (Chain-code pattern texture features) are used as the basis songket search in the database or referred to as a texture-based songket pattern recognition which is part of a content-based image retrieval (CBIR). This method consists of two parts: the first is the process of establishing databases feature Chain-code pattern texture songket and training process of pattern recognition using backpropagation neural network (BPNN), the second is the retrieval process to recognize the pattern songket (songket pattern recognition and retrieval). The proposed method is a combination of several algorithms: color image segmentation, binarization, cropping, edge detection/pattern, feature extraction pattern (probability widened chain-code datasets) and BPNN training and test. Results of tests on 40 different songket motifs with training data showing the level of accuracy of the proposed method. Results of tests on 40 songket motifs show a good degree of accuracy of proposed method where the precision value reached 98% and recall value reached 99%.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2200},
   doi = {10.18517/ijaseit.7.6.2200}
}

EndNote

%A Yuhandri, -
%A Madenda, Sarifuddin
%A Wibowo, Eri Prasetyo
%A Karmilasari, -
%D 2017
%T Pattern Recognition and Classification Using Backpropagation Neural Network Algorithm for Songket Motifs Image Retrieval
%B 2017
%9 songket; moore; chain code; CBIR; BPNN; neuron; precision value; recall value
%! Pattern Recognition and Classification Using Backpropagation Neural Network Algorithm for Songket Motifs Image Retrieval
%K songket; moore; chain code; CBIR; BPNN; neuron; precision value; recall value
%X This paper proposes a method of extraction, classification and pattern recognition songket cloth texture. Features Chain-code pattern texture (Chain-code pattern texture features) are used as the basis songket search in the database or referred to as a texture-based songket pattern recognition which is part of a content-based image retrieval (CBIR). This method consists of two parts: the first is the process of establishing databases feature Chain-code pattern texture songket and training process of pattern recognition using backpropagation neural network (BPNN), the second is the retrieval process to recognize the pattern songket (songket pattern recognition and retrieval). The proposed method is a combination of several algorithms: color image segmentation, binarization, cropping, edge detection/pattern, feature extraction pattern (probability widened chain-code datasets) and BPNN training and test. Results of tests on 40 different songket motifs with training data showing the level of accuracy of the proposed method. Results of tests on 40 songket motifs show a good degree of accuracy of proposed method where the precision value reached 98% and recall value reached 99%.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2200
%R doi:10.18517/ijaseit.7.6.2200
%J International Journal on Advanced Science, Engineering and Information Technology
%V 7
%N 6
%@ 2088-5334

IEEE

- Yuhandri,Sarifuddin Madenda,Eri Prasetyo Wibowo and - Karmilasari,"Pattern Recognition and Classification Using Backpropagation Neural Network Algorithm for Songket Motifs Image Retrieval," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 6, pp. 2343-2349, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.6.2200.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Yuhandri, -
AU  - Madenda, Sarifuddin
AU  - Wibowo, Eri Prasetyo
AU  - Karmilasari, -
PY  - 2017
TI  - Pattern Recognition and Classification Using Backpropagation Neural Network Algorithm for Songket Motifs Image Retrieval
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 6
Y2  - 2017
SP  - 2343
EP  - 2349
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - songket; moore; chain code; CBIR; BPNN; neuron; precision value; recall value
N2  - This paper proposes a method of extraction, classification and pattern recognition songket cloth texture. Features Chain-code pattern texture (Chain-code pattern texture features) are used as the basis songket search in the database or referred to as a texture-based songket pattern recognition which is part of a content-based image retrieval (CBIR). This method consists of two parts: the first is the process of establishing databases feature Chain-code pattern texture songket and training process of pattern recognition using backpropagation neural network (BPNN), the second is the retrieval process to recognize the pattern songket (songket pattern recognition and retrieval). The proposed method is a combination of several algorithms: color image segmentation, binarization, cropping, edge detection/pattern, feature extraction pattern (probability widened chain-code datasets) and BPNN training and test. Results of tests on 40 different songket motifs with training data showing the level of accuracy of the proposed method. Results of tests on 40 songket motifs show a good degree of accuracy of proposed method where the precision value reached 98% and recall value reached 99%.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2200
DO  - 10.18517/ijaseit.7.6.2200

RefWorks

RT Journal Article
ID 2200
A1 Yuhandri, -
A1 Madenda, Sarifuddin
A1 Wibowo, Eri Prasetyo
A1 Karmilasari, -
T1 Pattern Recognition and Classification Using Backpropagation Neural Network Algorithm for Songket Motifs Image Retrieval
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 6
YR 2017
SP 2343
OP 2349
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 songket; moore; chain code; CBIR; BPNN; neuron; precision value; recall value
AB This paper proposes a method of extraction, classification and pattern recognition songket cloth texture. Features Chain-code pattern texture (Chain-code pattern texture features) are used as the basis songket search in the database or referred to as a texture-based songket pattern recognition which is part of a content-based image retrieval (CBIR). This method consists of two parts: the first is the process of establishing databases feature Chain-code pattern texture songket and training process of pattern recognition using backpropagation neural network (BPNN), the second is the retrieval process to recognize the pattern songket (songket pattern recognition and retrieval). The proposed method is a combination of several algorithms: color image segmentation, binarization, cropping, edge detection/pattern, feature extraction pattern (probability widened chain-code datasets) and BPNN training and test. Results of tests on 40 different songket motifs with training data showing the level of accuracy of the proposed method. Results of tests on 40 songket motifs show a good degree of accuracy of proposed method where the precision value reached 98% and recall value reached 99%.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2200
DO  - 10.18517/ijaseit.7.6.2200