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All issues Volume 218 (2018) MATEC Web Conf., 218 (2018) 03012 References
Open Access
Issue
MATEC Web Conf.
Volume 218, 2018
The 1st International Conference on Industrial, Electrical and Electronics (ICIEE 2018)
Article Number 03012
Number of page(s) 9
Section Information Technology
DOI https://doi.org/10.1051/matecconf/201821803012
Published online 26 October 2018
  1. J. Hidayat, “The Art and Sustainable Aspects of Natural Dyeing in KANAWIDA Hand Drawn Batik (Green Batik),” IPTEK J. Proceeding Ser., (2014) [Google Scholar]
  2. R. Azhar, D. Tuwohingide, D. Kamudi, Sarimuddin, and N. Suciati, “Batik Image Classification Using SIFT Feature Extraction, Bag of Features and Support Vector Machine,” in Procedia Computer Science, (2015) [Google Scholar]
  3. A. H. Rangkuti, “Content based batik image classification using wavelet transform and fuzzy neural network,” J. Comput. Sci., (2014). [Google Scholar]
  4. A. H. Rangkuti, Z. E. Rasjid, and D. J. Santoso, “Batik Image Classification Using Treeval and Treefit as Decision Tree Function in Optimizing Content Based Batik Image Retrieval,” in Procedia Computer Science, (2015) [Google Scholar]
  5. R. Azhar, D. Tuwohingide, D. Kamudi, Sarimuddin, and N. Suciati, “Batik Image Classification Using SIFT Feature Extraction, Bag of Features and Support Vector Machine,” Procedia Comput. Sci., (2015) [Google Scholar]
  6. A. Kitipong, W. Rueangsirasak, and R. Chaisricharoen, “Classification system for traditional textile: Case study of the batik,” in 13th International Symposium on Communications and Information Technologies: Communication and Information Technology for New Life Style Beyond the Cloud, ISCIT 2013, (2013) [Google Scholar]
  7. F. Budiman, A. Suhendra, D. Agushinta, and A. Tarigan, “Wavelet decomposition levels analysis for Indonesia traditional batik classification,” J. Theor. Appl. Inf. Technol. (2016) [Google Scholar]
  8. T. Handhayani, “Batik Lasem images classification using voting feature intervals 5 and statistical features selection approach,” Proceeding - 2016 Int. Semin. Intell. Technol. Its Appl. ISITIA 2016 Recent Trends Intell. Comput. Technol. Sustain. Energy, pp. 13–16, (2017) [Google Scholar]
  9. I. Nurhaida, R. Manurung, and A. M. Arymurthy, “Performance comparison analysis features extraction methods for Batik recognition,” in 2012 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2012 - Proceedings, (2012) [Google Scholar]
  10. N. Suciati, A. Kridanto, M. F. Naufal, M. Machmud, and A. Y. Wicaksono, “Fast discrete curvelet transform and HSV color features for batik image clansificotlon,” in Proceedings of 2015 International Conference on Information and Communication Technology and Systems, ICTS 2015, (2016) [Google Scholar]
  11. H. Wijayanto, “Klasifikasi Batik Menggunakan Metode K-Nearest Neighbour Berdasarkan Gray Level Co-Occurrence Matrices ( Glcm ),” Klasifikasi Batik Menggunakan Metod. K-Nearest Neighb. Berdasarkan Gray Lev. Co-Occurrence Matrices ( Glcm ), (2015) [Google Scholar]
  12. C. S. K. Aditya, M. Hani'Ah, R. R. Bintana, and N. Suciati, “Batik classification using neural network with gray level co-occurence matrix and statistical color feature extraction,” in Proceedings of 2015 International Conference on Information and Communication Technology and Systems, ICTS 2015, (2016) [Google Scholar]
  13. F. U. Karimah and A. Harjoko, “Classification of batik kain besurek image using speed up robust features (SURF) and gray level co-occurrence matrix (GLCM),” in Communications in Computer and Information Science, (2017) [Google Scholar]
  14. F. Budiman, A. Suhendra, D. Agushinta, and A. Tarigan, “Determination of SVM-RBF kernel space parameter to optimize accuracy value of Indonesian Batik images classification,” J. Comput. Sci., (2017) [Google Scholar]
  15. A. A. Kasim, R. Wardoyo, and A. Harjoko, “The selection feature for batik motif classification with information gain value,” in Communications in Computer and Information Science, (2017) [Google Scholar]
  16. T. Handhayani, “Batik Lasem images classification using voting feature intervals 5 and statistical features selection approach,” in Proceeding - 2016 International Seminar on Intelligent Technology and Its Application, ISITIA 2016: Recent Trends in Intelligent Computational Technologies for Sustainable Energy, (2017) [Google Scholar]

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