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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