Classification of Indonesian Batik Using Deep Learning Techniques and Data Augmentation

Agastya, I Made Artha; Setyanto, Arief

doi:10.1109/icitisee.2018.8720990

Cited by 30 publications

(21 citation statements)

References 14 publications

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“…Hal ini akan kurang efektif dalam menjaga kelestarian budaya daerah yang tentunya menjadi identitas dari daerah itu sendiri terhadap kekayaan budaya yang dimiliki. Beberapa penelitian telah dilakukan untuk mengatasi hal tersebut dimana salah satunya adalah mengembangkan sistem yang dapat mengenali atau mengidentifikasi motif batik berbasiskan pengolahan citra digital, seperti yang dilakukan oleh Agastya dkk dan Khasanah dkk yang mengimplementasikan data augmentasi dan algoritma deep learning untuk mengklasifikasikan motif batik Ceplok, Kawung, Lereng, Nitik dan Parang [2], [3]. Dengan metode yang diusulkan Agastya dkk, mampu mendeteksi kelima motif batik tersebut dengan akurasi mencari 90%.…”

Section: Pendahuluanunclassified

Pengenalan Motif Batik Banyuwangi Berdasarkan Fitur Grey Level Co-Occurrence Matrix

Hakim

Kristanto

Yusuf

et al. 2022

JTI

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Batik is one of Indonesia's cultural heritages that is recognized around the world and has existed since the colonial era. Indonesia has a variety of different batik pattern in every Indonesia's region. It causes many ordinary people and tourists to become harder to identify and recognize the existing patterns. Banyuwangi regency itself has more than 10 batik patterns, including the Gajah Oling pattern which in the oldest batik pattern. For preserving the culture and supporting the growing tourism aspect in Banyuwangi, this study developed a system for recognizing Banyuwangi batik patterns based on digital image processing. This system is built using python language and is able to recognize three classes of Banyuwangi batik patterns, such as Gajah Oling, Kopi Pecah and other Banyuwangi batik patterns. This system proposes Gray Level Co-occurrence Matrix (GLCM) as feature extraction method and k-Nearest Neighbors (kNN) as classification method. Based on the experiments that have been carried out, the optimal accuracy is 87,5% with the K parameter of kNN is 9.

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Section: Pendahuluanunclassified

Pengenalan Motif Batik Banyuwangi Berdasarkan Fitur Grey Level Co-Occurrence Matrix

Hakim

Kristanto

Yusuf

et al. 2022

JTI

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“…Generally, in the image recognition process, the features generated from the feature extraction process are used as input to train prediction models using traditional machine learning algorithms. Although this approach is very popular, with the advent of deep learning, especially with CNN, new classification methods that do not require manual feature extraction are starting to be widely applied to batik classification problems [29]- [34]. However, despite the vast number of studies related to batik, only a few studies have focused on predicting batik-making methods, with others more frequently aimed for batik pattern classification.…”

Section: Bulletin Of Electr Eng and Infmentioning

confidence: 99%

Identification of batik making method from images using convolutional neural network with limited amount of data

2021

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This study aims to apply the convolutional neural network (CNN) to classify batik based on its manufacturing method, namely Batik Tulis which are hand drawn, Batik Cap where stamps are used to create the pattern, and Batik Printing which are printed using textile printing machine. We collected 40 images for each type of batik with a total of 120 images. To speed up and simplify the model building process, we implemented transfer learning with 3 basic CNN model architectures, namely ResNet, DenseNet, and VGG with batch normalization. We also experimented with building a new dataset by breaking each image down into 30 smaller images. Image augmentation was also used to prevent overfitting as well as to provide variations in the training data. The experimental results with 5-fold cross validation show that densenet169 gives the best results on the original dataset with an accuracy of 79.17% while vgg13_bn shows the best performance on the modified dataset with an accuracy of 87.61%. All models showed an increase in performance when using the modified dataset, except densenet169 which did not show a significant difference in performance.

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“…The use of deep learning methods has also been widely used using the CNN method [11], [12] dan Fuzzy Neural Network [13]. The use of CNNs with different architectures such as VGG-13 [14], VGG-16 [15]- [17] dan VGG-19 [18] has also been used to automate the batik motifs classification. The studies that many researchers have done give good and bad results.…”

Section: Introductionmentioning

confidence: 99%

“…Gulthom [15] uses CNN with VGG-16 architecture as feature extraction and SoftMax as its classifier with patching method using total data of 2092 images with five classes resulting in an accuracy of 89 % ± 7. Agastya [18] used the Gulthom dataset, which uses CNN with VGG-19 architecture as feature extraction and SoftMax as its classifier. Implementing the same patching method with 900 images with five classes improves accuracy to 89.3 % better than previous research [15] by 0.3 %.…”

Section: Introductionmentioning

confidence: 99%

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Batik Classification Using Convolutional Neural Network with Data Improvements

Meranggi

Yudistira

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2022

JOIV : Int. J. Inform. Visualization

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Batik is one of the Indonesian cultures that UNESCO has recognized. Batik has a variety of unique and distinctive patterns that reflect the area of origin of the batik motif. Batik motifs usually have a 'core motif' printed repeatedly on the fabric. The entry of digitization makes batik motif designs more diverse and unique. However, with so many batik motifs spread on the internet, it is difficult for ordinary people to recognize the types of batik motifs. This makes an automatic classification of batik motifs must continue to be developed. Automation of batik motif classification can be assisted with artificial intelligence. Machine learning and deep learning have produced much good performance in image recognition. In this study, we use deep learning based on a Convolutional Neural Network (CNN) to automate the classification of batik motifs. There are two datasets used in this study. The old dataset comes from a public repository with 598 data with five types of motifs. Meanwhile, the new dataset updates the old dataset by replacing the anomalous data in the old dataset with 621 data with five types of motifs. The lereng motif is changed to pisanbali due to the difficulty of obtaining the lereng motif. Each dataset was divided into three ways: original, balance patch, and patch. We used ResNet-18 architecture, which used a pre-trained model to shorten the training time. The best test results were obtained in the new dataset with the patch way of 88.88 % ±0.88, and in the old dataset, the best accuracy was found in the patch way on the test data of 66.14 % ±3.7. The data augmentation in this study did not significantly affect the accuracy because the most significant increase in accuracy is only up to 1.22%.

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Classification of Indonesian Batik Using Deep Learning Techniques and Data Augmentation

Cited by 30 publications

References 14 publications

Pengenalan Motif Batik Banyuwangi Berdasarkan Fitur Grey Level Co-Occurrence Matrix

Pengenalan Motif Batik Banyuwangi Berdasarkan Fitur Grey Level Co-Occurrence Matrix

Identification of batik making method from images using convolutional neural network with limited amount of data

Batik Classification Using Convolutional Neural Network with Data Improvements

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