Batik Pattern Classification using Naïve Bayes Method Based on Texture Feature Extraction

Imam Riadi(1*), Abdul Fadlil(2), Izzan Julda D.E Purwadi Putra(3),

(1) Universitas Ahmad Dahlan
(2) Universitas Ahmad Dahlan
(3) Universitas Ahmad Dahlan
(*) Corresponding Author
DOI: https://doi.org/10.23917/khif.v9i1.21207

Abstract

One of the arts in Surakarta culture is batik cloth. A batik is a form of heritage from the nation's ancestors whose manufacturing process must use specific tools and materials. Surakarta's typical batik has many patterns and motifs, such as Sawat, Satriomanah, and Semenrante. The pattern is a picture framework whose results will display the type of batik. A batik may resemble one type and another, so a classification technique is needed to determine the type of batik. This study aims to develop a classification method for batik cloth using the Naïve Bayes classification technique. The feature extraction used is the Gray Level Co-Occurrence Matrix (GLCM) to obtain texture values in each image. The stages in this research include pre-processing, feature extraction, classification, and testing. The training data in this study were 200 images for each Sawat, Satriomanah, and Sementrante class obtained from the data augmentation method by flipping, zooming, cropping, shifting, and changing the brightness of the images. The total sample data is 600 images. The amount of training data and data testing was divided three times (60% training and 40% testing), (70% training and 30% testing), and (80% training and 20% testing) for accuracy. In this study, the Naïve Bayes method using WEKA 3.8.6 tools obtained the best accuracy of 97.22% using a 70% percentage split compared to using 80% and 60% percentage splits with a result of 96.66%, this difference occurs due to differences in training data and test data. The results of this study indicate that the Naïve Bayes method can be used to classify batik cloth patterns based on texture feature extraction.

Keywords

Classification, GLCM, Naïve Bayes, Surakarta Batik Pattern

Full Text:

Accepted PDF

References

A. Yudi Aprianingrum and A. Hayati Nufus, “Batik Indonesia, Pelestarian Melalui Museum,” Pros. Semin. Nas. Ind. Kerajinan dan Batik, pp. 1–14, 2021.

T. Bariyah, M. A. Rasyidi, and N. Ngatini, “Convolutional Neural Network untuk Metode Klasifikasi Multi-Label pada Motif Batik,” Techno.Com, vol. 20, no. 1, pp. 155–165, 2021, doi: 10.33633/tc.v20i1.4224.

L. M. Hakim, “Batik Sebagai Warisan Budaya Bangsa dan Nation Brand Indonesia,” Nation State J. Int. Stud., vol. 1, no. 1, pp. 61–90, 2018, doi: 10.24076/nsjis.2018v1i1.90.

H. Rante and M. Safrodin, “Learning Batik through Gaming,” Int. Electron. Symp. Knowl. Creat. Intell. Comput., vol. 227, no. 12, pp. 1011–1012, 2018, doi: 10.1038/s41415-019-1106-9.

K. Oentoro, S. Y. Amijaya, and T. Seliari, “Analisis Pengembangan Wirausaha Batik Tradisional di Sekitar Embung Langensari Yogyakarta,” Res. Fair Unisri, vol. 3, no. 1, pp. 1–7, 2019.

S. Hadi, I. Qiram, and G. Rubiono, “Exotic Heritage from Coastal East Java of Batik Bayuwangi,” IOP Conf. Ser. Earth Environ. Sci., vol. 156, no. 1, 2018, doi: 10.1088/1755-1315/156/1/012018.

R. Ayunda, B. Maneshakerti, F. Hukum, and U. I. Batam, “Perlindungan Hukum Atas Motif Tradisional Baik Batam,” J. Pendidik. Kewarganegaraan Undiksha, vol. 9, no. 3, pp. 822–833, 2021.

M. E. Widiana, Batik Daerah Devisa Negara. CV. Pena Persada, 2019.

T. A. P. Sidhi, B. Y. Dwiandiyanta, and F. K. S. Dewi, “Batik Motifs Detection Using Pattern Recognition Method,” J. Buana Inform., vol. 11, no. 1, p. 55, 2020, doi: 10.24002/jbi.v11i1.3234.

Y. Gultom, A. M. Arymurthy, and R. J. Masikome, “Batik Classification using Deep Convolutional Network Transfer Learning,” J. Ilmu Komput. dan Inf., vol. 11, no. 2, p. 59, 2018, doi: 10.21609/jiki.v11i2.507.

S. Saputra, A. Yudhana, and R. Umar, “Identifikasi Kesegaran Ikan Menggunakan Algoritma KNN Berbasis Citra Digital,” Krea-TIF J. Tek. Inform., vol. 10, no. 1, pp. 1–9, 2022, doi: 10.32832/kreatif.v10i1.6845.

R. Umar, I. Riadi, A. Hanif, and S. Helmiyah, “Identification of speaker recognition for audio forensic using k-nearest neighbor,” Int. J. Sci. Technol. Res., vol. 8, no. 11, pp. 3846–3850, 2019.

S. Saputra, A. Yudhana, and R. Umar, “Implementation of Naïve Bayes for Fish Freshness Identification Based on Image Processing,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 3, pp. 412–420, 2022, doi: 10.29207/resti.v6i3.4062.

H. Azarmdel, S. S. Mohtasebi, A. Jafari, and A. Rosado Muñoz, “Developing an orientation and cutting point determination algorithm for a trout fish processing system using machine vision,” Comput. Electron. Agric., vol. 162, no. January, pp. 613–629, 2019, doi: 10.1016/j.compag.2019.05.005.

R. Umar, I. Riadi, and M. Miladiah, “Sistem Identifikasi Keaslian Uang Kertas Rupiah Menggunakan Metode K-Means Clustering,” Techno.Com, vol. 17, no. 2, pp. 179–185, 2018, doi: 10.33633/tc.v17i2.1681.

A. Yudhana, R. Umar, and S. Saputra, “Fish Freshness Identification Using Machine Learning: Performance Comparison of k-NN and Naïve Bayes Classifier,” J. Comput. Sci. Eng., vol. 16, no. 3, pp. 153–164, 2022, doi: 10.5626/JCSE.2022.16.3.153.

K. Nugroho and E. Winarno, “Spoofing Detection of Fake Speech Using Deep Neural Network Algorithm,” Int. Semin. Appl. Technol. Inf. Commun., 2022, doi: 10.1109/iSemantic55962.2022.9920401.

F. N. Suteja, E. W. Hidayat, and N. Widiyasono, “Implementation of Image Enhancement Algorithm for Image Forensics using Mathlab,” J. Online Inform., vol. 4, no. 2, p. 79, 2020, doi: 10.15575/join.v4i2.314.

A. P. A. Masa and H. Hamdani, “Klasifikasi Motif Citra Batik Menggunakan Convolutional Neural Network Berdasarkan K-means Clustering,” J. Media Inform. Budidarma, vol. 5, no. 4, p. 1292, 2021, doi: 10.30865/mib.v5i4.3246.

R. A. Surya, A. Fadlil, and A. Yudhana, “Identification of Pekalongan Batik Images Using Backpropagation Method,” J. Phys. Conf. Ser., vol. 1373, no. 1, 2019, doi: 10.1088/1742-6596/1373/1/012049.

A. Septiarini, Rizqi Saputra, Andi Tejawati, and Masna Wati, “Deteksi Sarung Samarinda Menggunakan Metode Naive Bayes Berbasis Pengolahan Citra,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 5, pp. 927–935, 2021, doi: 10.29207/resti.v5i5.3435.

E. H. Rachmawanto et al., “Eggs classification based on egg shell image using k-nearest neighbors classifier,” Proc. - 2020 Int. Semin. Appl. Technol. Inf. Commun. IT Challenges Sustain. Scalability, Secur. Age Digit. Disruption, iSemantic 2020, pp. 50–54, 2020, doi: 10.1109/iSemantic50169.2020.9234305.

A. E. Minarno, Y. Azhar, F. D. Setiawan Sumadi, and Y. Munarko, “A Robust Batik Image Classification using Multi Texton Co-Occurrence Descriptor and Support Vector Machine,” 2020 3rd Int. Conf. Intell. Auton. Syst. ICoIAS 2020, pp. 51–55, 2020, doi: 10.1109/ICoIAS49312.2020.9081833.

I. U. W. Mulyono et al., “Parijoto Fruits Classification using K-Nearest Neighbor Based on Gray Level Co-Occurrence Matrix Texture Extraction,” J. Phys. Conf. Ser., vol. 1501, no. 1, 2020, doi: 10.1088/1742-6596/1501/1/012017.

H. T. Zaw, N. Maneerat, and K. Y. Win, “Brain tumor detection based on Naïve Bayes classification,” Proceeding - 5th Int. Conf. Eng. Appl. Sci. Technol. ICEAST 2019, pp. 1–4, 2019, doi: 10.1109/ICEAST.2019.8802562.

V. R. Balaji, S. T. Suganthi, R. Rajadevi, V. Krishna Kumar, B. Saravana Balaji, and S. Pandiyan, “Skin disease detection and segmentation using dynamic graph cut algorithm and classification through Naive Bayes classifier,” Meas. J. Int. Meas. Confed., vol. 163, p. 107922, 2020, doi: 10.1016/j.measurement.2020.107922.

A. Nagpal and G. Gabrani, “Python for Data Analytics, Scientific and Technical Applications,” Proc. - 2019 Amity Int. Conf. Artif. Intell. AICAI 2019, pp. 140–145, 2019, doi: 10.1109/AICAI.2019.8701341.

P. Osipovs, “Classification tree applying for automated CV filtering in transport company,” Procedia Comput. Sci., vol. 149, pp. 406–414, 2019, doi: 10.1016/j.procs.2019.01.155.

D. Merlini and M. Rossini, “Text categorization with WEKA: A survey,” Mach. Learn. with Appl., vol. 4, no. November 2020, p. 100033, 2021, doi: 10.1016/j.mlwa.2021.100033.

I. M. Achievements, “Classification Based on Machine Learning Methods for Identification of Image Matching Achievements,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 158, pp. 198–206, 2022.

M. A. Masril and R. Noviardi, “Analisa Morfologi Dilasi untuk Perbaikan Kualitas Citra Deteksi Tepi pada Pola Batik Menggunakan Operator Prewitt dan Laplacian of Gaussian,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 6, pp. 9–11, 2020, doi: 10.29207/resti.v4i6.2601.

B. Sugandi and S. Dewi, “Sistem Inspeksi Kecacatan pada Kaleng Menggunakan Filter Warna HSL dan Template Matching,” Khazanah Inform. J. Ilmu Komput. dan Inform., vol. 4, no. 2, p. 124, 2018, doi: 10.23917/khif.v4i2.7119.

Y. Sari, M. Alkaff, and R. A. Pramunendar, “Classification of coastal and Inland Batik using GLCM and Canberra Distance,” AIP Conf. Proc., vol. 1977, no. June 2018, 2018, doi: 10.1063/1.5042901.

P. N. Andono and E. H. Rachmawanto, “Evaluasi Ekstraksi Fitur GLCM dan LBP Menggunakan Multikernel SVM untuk Klasifikasi Batik,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 1, pp. 1–9, 2021, doi: 10.29207/resti.v5i1.2615.

A. Yudhana, D. Sulistyo, and I. Mufandi, “GIS-based and Naïve Bayes for nitrogen soil mapping in Lendah, Indonesia,” Sens. Bio-Sensing Res., vol. 33, p. 100435, 2021, doi: 10.1016/j.sbsr.2021.100435.

Z. Zhang, “Introduction to machine learning: K-nearest neighbors,” Ann. Transl. Med., vol. 4, 2016, doi: 10.21037/atm.2016.03.37.

N. Saurina, T. Rahayuningsih, and L. Retnawati, “Analisis Sentimen Ulasan Pelanggan Batik Ecoprint Menggunakan Naïve Bayes Dan Knn Classifier,” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 9, no. 2, pp. 1532–1542, 2022, doi: 10.35957/jatisi.v9i2.1483.

J. W. G. Putra, Pengenalan Konsep Pembelajaran Mesin dan Deep Learning, vol. 4. 2020. [Online]. Available: https://wiragotama.github.io/

Article Metrics

Abstract view(s): 441 time(s)
Accepted PDF: 388 time(s)

Refbacks