Classification Of Batik Images Using Multilayer Perceptron With Histogram Of Oriented Gradient Feature Extraction


  • Nardianti Dewi Girsang Medan Area University
  • Muhathir Medan Area University


Keywords: Batik, Image, Histogram Of Oriented Gradient, Multilayer Perceptron.


Abstract. Batik is one of the hereditary cultural heritages which has a high aesthetic value and a deep philosophy. Batik is one of the cultural icons from Indonesia which was awarded as a cultural heritage from UNESCO on October 2, 2009. Currently, Indonesian batik has various types of motifs and different patterns, which are scattered in Indonesia with their names and meanings. The many batik patterns in Indonesia make it difficult to identify batik motifs, especially for the common people. To overcome this problem, it is necessary to have a batik classification system with a high level of accuracy, so that everyone can recognize the batik pattern easily. In this study, the Histogram Of Oriented Gradient method was used as a feature extraction process to obtain batik density and Multilayer Perceptron as a classification method to determine the level of accuracy. The result of the level of accuracy obtained for each batik motif has a different level of accuracy. This is because the batik motifs have unique patterns and shapes that are not specific. The level of income obtained was 83.4 %. From the results of the study concluded that the use of the Histogram Of Oriented Gradient method as a feature extraction method, the Multilayer Perceptron as a classifier can be applied for image classification on batik. 


Download data is not yet available.


Agirre-Basurko, E., G.Ibarra-Berastegi & Madariaga, I., 2006. Regression and multilayer perceptron-based models to forecast hourly O3 and NO2 levels in the Bilbao area. Environmental Modelling & Software, 21(4), pp. 430-446. Azhar, R. et al., 2015. Batik Image Classification Using SIFT Feature Extraction, Bag of Features, and Support Vector Machine. Procedia Computer Science, Volume 72, pp. 24-30. Basheer, I. & Hajmeer, M., 2000. Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods, 43(1), pp. 3-31. Dalal, N. & Triggs, B., 2005. Histograms of oriented gradients for human detection. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp. 886-893. Devella, S., 2018. Pengenalan Irismenggunakan Ekstraksi Fitur Histogram of Oriented Gradient. Jurnal Teknik Informatika dan Sistem Informasi, Volume 4, pp. 124-134. Efendi, A. & M, N. G., 2016. PENGEMBANGAN MOTIF BATIK BONGKOL DI DESA SUMURGUNG KABUPATEN TUBAN. Jurnal Pendidikan Seni Rupa, 4(2), p. 221–224 . Ewees, A. A. et al., 2020. Improving multilayer perceptron neural network using a chaotic grasshopper optimization algorithm to forecast iron ore price volatility. Resources Policy, Volume 65, pp. 1-12. Fortin, J. G., Anctil, F. & Parent, L., 2014. Comparison of Multiple-Layer Perceptrons and Least Squares Support Vector Machines for Remote-Sensed Characterization of In-Field LAI Patterns – A Case Study with Potato. Canadian Journal Of Remote Sensing, 40(2), pp. 75-84. GAIKWAD, N. B., TIWARI, V., KESKAR, A. & SHIVAPRAKASH, N. C., 2019. Efficient FPGA Implementation of Multilayer Perceptron for Real-Time Human Activity Classification. IEEE Access, Volume 7, pp. 26696-26706.
Gultom, Y., Masikome, R. J. & Arymurthy, A. M., 2018. BATIK CLASSIFICATION USING DEEP CONVOLUTIONAL NETWORK TRANSFER LEARNING. Journal of a Science and Information, 11(2), pp. 59-66. Guo, W., Wei, H., Zhao, J. & Zhang, K., 2015. Theoretical and numerical analysis of learning dynamics near singularity in multilayer perceptrons. Neurocomputing, Volume 151, pp. 390-400. Huanga, F., Huang, J., Jiang, S. & Zhouc, C., 2017. Landslide displacement prediction based on multivariate chaotic model and extreme learning machine. Engineering Geology, Volume 218, pp. 173-186. Huang, F., Luo, X. & Liu, W., 2017. Stability Analysis of Hydrodynamic Pressure Landslides with Different Permeability Coefficients Affected by Reservoir Water Level Fluctuations and Rainstorms. Water, 9(7), pp. 1-16. Kasim, A. A. & Harjoko, R. W. a. A., 2017. Batik Classification with Artificial Neural Network Based on Texture-Shape Feature of Main Ornament. I.J. Intelligent Systems and Applications, Volume 6, pp. 55-65. KUSUMA, P. D., 2017. FIBROUS ROOT MODEL IN BATIK PATTERN GENERATION. Journal of Theoretical and Applied Information Technology , Volume 95, pp. 3260-3269. LI, Y. et al., 2019. Multilayer Perceptron Method to Estimate Real-World Fuel Consumption Rate of Light Duty Vehicles. IEEE Access, Volume 7, pp. 63395-63402. Mawan, R., 2020. Klasifikasi Motif Batik Menggunakan Convolutional Neural Network. JNANALOKA, VI(1-2), pp. 45-50. Muhathir, Mawengkang, H. & Ramli, M., 2017. KOMBINASI ZFISHER TRANSFORM DAN BRAY CURTIS DISTANCE UNTUK PENGENALAN POLA HURUF JAR PADA CITRA AL-QURAN. Jurnal Bisman Info, 4(1). Muhathir, Rizal, R. A., Sihotang, J. S. & Gultom, R., 2019. Comparison of SURF and HOG extraction in classifying the blood image of malaria parasites using SVM. ICoSNIKOM, pp. 1-6. Muhathir, Sibarani, T. T. S. & Al-Khowarizmi, 2020. Analysis K-Nearest Neighbors(KNN)in Identifying Tuberculosis Disease (Tb) By Utilizing Hog Feature Extraction. Al'adzkiya International of Computer Science and Information Technology (AIoCSIT) Journal, Volume 1, pp. 33-38. Muwafiq, A. & Pamungkas, D. P., 2020. Implementasi Metode Convolutional Neural Network Untuk Klasifikasi Motif Batik. Seminar Nasional Inovasi Teknologi, Issue 2549-7952, pp. 121126. Nurainun, Heriyana & Rasyimah, 2008. ANALISIS INDUSTRI BATIK DI INDONESIA. Fokus Ekonomi, Volume 7, pp. 124-135. Purwaningsih, N., 2016. PENERAPAN MULTILAYER PERCEPTRON UNTUK KLASIFIKASI JENIS KULIT SAPI TERSAMAK. Jurnal TEKNOIF, Volume 4, pp. 1-7. Putri, R. A. & Rochmawati, N., 2019. Penerapan Algoritma Support Vector Machine untuk Klasifikasi Motif Citra Batik Solo Berdasarkan Fitur Multi-Autoencoders. JINACS, Volume 01, pp. 56-63. Rangkuti, A. H., 2014. CONTENT BASED BATIK IMAGE CLASSIFICATION USING WAVELET TRANSFORM AND FUZZY NEURAL NETWORK. Journal of Computer Science 10, Volume 4, pp. 604-613. Santoso, M. H., Larasati, D. A. & Muhathir, 2020. Wayang Image Classification using MLP Method and GLCM Feature Extraction. JCoSITTE, Volume 1, pp. 111-120. Steelyana, E., 2012. Batik, a beautiful cultural heritage that preserve culture and support economic development in indonesia. Binus Business Review, 3(1), pp. 116-131. SYAHPUTRA, 1. & SOESANTI, I., 2016. Design of Automatic Electric Batik Stove for Batik Industry. Journal of Theoretical and Applied Information Technology , Volume 87, pp. 167-175. Tanjung, J. P. & Muhathir, 2020. Classification of facial expressions using SVM and HOG. JITE (Journal of Informatics and Telecommunication Engineering), 3(2), pp. 210-215. Tresnadi, C. & Sachari, A., 2015. Identification of Values of Ornaments in Indonesian Batik in Visual Content of Nitiki Game. Journal of Arts & Humanities, Volume 4, pp. 25-39.
Yohannes & Rivan, M. E. A., 2020. Penggunaan Global Contrast Saliencydan Histogram of Oriented GradientSebagai Fitur untuk Klasifikasi Jenis Hewan Mamalia. PETIR, Volume 13, pp. 80-85.




How to Cite

Girsang, N. D., & Muhathir. (2021). Classification Of Batik Images Using Multilayer Perceptron With Histogram Of Oriented Gradient Feature Extraction. Proceeding International Conference on Science and Engineering, 4, 197–204. Retrieved from