Application System for Identification of Surakarta Traditional Batik Images (SABATARA)
DOI:
10.33395/sinkron.v4i1.10202Keywords:
Characteristic; Batik Patterns; IdentificationAbstract
Surakarta Batik is a traditional cloth in Indonesia that has been designated as an intangible cultural heritage by the Ministry of Education and Culture. The Surakarta Batik Pattern has characteristics and has a story in each style. The method used affects the accuracy of each pattern in the Surakarta batik image. Image data used for training data are 100 image data with a size of 256 x 256 pixels, with test image data used as many as 20 image data. Improving the quality of the image using contrast stretching, the output is processed to separate objects with the background using adaptive thresholding. The obtained object is added by the canny process and calculated using the Gray Level Co-Occurrence Matrix to obtain the characteristics of each image. The characteristics used are four variables (energy, contrast, homogeneity, and correlation). The resulting variable is used as input to the classification using backpropagation. The test results obtained an accuracy rate of 95%, with an error rate of 0.05%.
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References
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<a href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Klasifikasi+Motif+Batik+Banyuwangi+Menggunakan+Metode+Ekstraksi+Ciri+Wavelet+Dan+Metode+Klasifikasi+Fuzzy+Logic&btnG=">Google Scholar</a>
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<a href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=+Batik+Image+Retrieval+Based+on+Enhanced+Micro-Structure+Descriptor&btnG=">Google Scholar</a>
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Padmo A.M, A. (2016). Segmentasi Citra Batik Berdasarkan Fitur Tekstur Menggunakan Metode Filter Gabor Dan K-Means Clustering. Jurnal Informatika.
<a href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Segmentasi+Citra+Batik+Berdasarkan+Fitur+Tekstur+Menggunakan+Metode+Filter+Gabor+Dan+K-Means+Clustering&btnG=">Google Scholar</a>
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Purba, B. (2017). Aplikasi Perbaikan Kualitas Citra Hasil Penginderaan Jauh (Remote Sensing) Dengan Metode Contrast Stretching. Jurnal Times (Technology Informatics & Computer System), 26-36.
R, I. D. (2016). Klasifikasi Batik Menggunakan KNN Berbasis Wavelet . Seminar Nasional Teknologi Informasi dan Komunikasi 2016 (SENTIKA 2016) (2016) Yogyakarta.
Rahayuda, I. G. (2015). Texture Analysis on Image Motif of Endek Bali using K-Nearest Neighbor Classification Method. International Journal of Advanced Computer Science and Applications, 205-211.
<a href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Texture+Analysis+on+Image+Motif+of+Endek+Bali+using+K-Nearest+Neighbor+Classification+Method&btnG=">Google Scholar</a>
Rangkuti, A. H. (2015). Batik image classification using treeval and treefit as decision tree function in optimizing content based batik image retrieval. Procedia Computer Science, (pp. 577-583).
<a href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Batik+image+classification+using+treeval+and+treefit+as+decision+tree+function+in+optimizing+content+based+batik+image+retrieval&btnG=">Google Scholar</a>
Sari, Y. (2018). Classification of Coastal and Inland Batik using GLCM and Canberra Distance. AIP Conference Proceedings.
<a href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Classification+of+Coastal+and+Inland+Batik+using+GLCM+and+Canberra+Distance&btnG=">Google Scholar</a>
Sari, Y. (2018). Klasifikasi Pengenalan Motif Batik Berbasis Image Retrieval. Jukung Jurnal Teknik Lingkungan, 27-33.
Sianturi, F. A. (2015). Penerapan Metode Contrast Stretching Untuk Peningkatan Kualitas Citra Bidang Biomedis. Jurnal Matik Penusa (Jurnal Manajemen dan Informatika Pelita Nusantara), 42-47.
<a href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&scioq=+Klasifikasi+Pengenalan+Motif+Batik+Berbasis+Image+Retrieval&q=Implementasi+Teknik+Thresholding+Pada+Segmentasi+Citra+Digital&btnG=">Google Scholar</a>
Sinaga, A. S. (2017). Implementasi Teknik Thresholding Pada Segmentasi Citra Digital. Jurnal Mantik Penusa (Jurnal Manajemen Dan Informatika Pelita Nusantara), 48-51.
<a href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&scioq=+Klasifikasi+Pengenalan+Motif+Batik+Berbasis+Image+Retrieval&q=Implementasi+Teknik+Thresholding+Pada+Segmentasi+Citra+Digital&btnG=">Google Scholar</a>
Syahrin, A. (2015). The Batik Motif.
WHO. (2004). Developing health management information systems: a practical guide for developing countries. Dev. Heal. Manag. Inf. Syst. a Pract. Guid. Dev. Ctries., 60.
Wicaksono, A. Y. (2017). Modified Convolutional Neural Network Architecture for Batik Motif Image Classification. IPTEK Journal of Science, 26-30.
<a href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&scioq=+Klasifikasi+Pengenalan+Motif+Batik+Berbasis+Image+Retrieval&q=Modified+Convolutional+Neural+Network+Architecture+for+Batik+Motif+Image+Classification&btnG=">Google Scholar</a>
Yohanes Gultom, R. J. (2018). Batik Classification Using Deep Convolutional Network Transfer Learning. Jurnal Ilmu Komputer dan Informasi (Journal of a Science and Information), 59-66.
<a href="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&scioq=+Klasifikasi+Pengenalan+Motif+Batik+Berbasis+Image+Retrieval&q=+Batik+Classification+Using+Deep+Convolutional+Network+Transfer+Learning&btnG=">Google Scholar</a>
Yunari, N. (2018). Extraction Gray Level Co-occurrence Matrix ( GLCM ) and Learning Vector Quantization ( LVQ ). Journal of Telecommunication, Electronic and Computer Engineering, 67-71.
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Kusanti, J., & T.S, R. A. (2019). Application System for Identification of Surakarta Traditional Batik Images (SABATARA). Sinkron : Jurnal Dan Penelitian Teknik Informatika, 4(1), 5-12. https://doi.org/10.33395/sinkron.v4i1.10202
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