Batik Image Research Articles

PERBANDINGAN JARAK EUCLIDEAN, CITYBLOCK, MINKOWSKI, CANBERRA, DAN CHEBYSHEV DALAM SISTEM TEMU KEMBALI CITRA BATIK

Batik is a highly valuable cultural heritage in Indonesia, showcasing a rich diversity of motifs with deep meaning and aesthetics. To enhance the accessibility and utilization of batik collections, an efficient image retrieval system is essential. This study compares distance measurement methods in a batik image retrieval system: Euclidean, Cityblock, Minkowski, Canberra, and Chebyshev, using a combination of color and texture features. The dataset comprises 50 types of batik images. The results show that the Cityblock method achieves the highest Mean Average Precision (MAP) of 97.71, followed by Canberra with MAP 96.87. The Euclidean method also performs well with a MAP of 94.56, while Minkowski and Chebyshev have lower MAP values of 92.93 and 90.89, respectively. Chebyshev experiences the largest MAP drop when images are rotated (5.98), while Cityblock demonstrates the best resistance to rotation with the smallest MAP drop (1.51). This research successfully developed a Content-Based Image Retrieval (CBIR) system with a GUI in MATLAB and suggests integrating the latest image processing and machine learning techniques for further enhancement.

Performance Comparison of GLCM Features and Preprocessing Effect on Batik Image Retrieval

The use of the Grey-Level Co-occurrence Matrix (GLCM) for feature extraction in image retrieval with complex motifs, such as batik images, has been widely used. Some features often extracted include energy, entropy, correlation, and contrast. Other than these four features, the addition of dissimilarity and homogeneity features to the GLCM method is proposed in this study. Preprocessing methods such as Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE) are also used to see whether the two methods can increase the precision value of the retrieval results. This study used the Batik 300 dataset, which consists of 50 classes. Batik was chosen because this type of image has complex patterns and motifs so that it will maximize the role of the GLCM method itself. In addition, Batik is also a world heritage art, so its sustainability needs to be maintained. The test results show that adding dissimilarity and homogeneity features and using the CLAHE method in the preprocessing step can improve model performance. Combining these two methods has produced higher precision values than not using either. Batik, a globally recognized art form, holds the status of a world heritage, necessitating the preservation of its sustainability. Test results have demonstrated that incorporating dissimilarity and homogeneity features, alongside using the CLAHE method during the preprocessing stage, leads to enhanced model performance. The amalgamation of these two methods has yielded precision values that surpass those achieved when either method is used in isolation.

Classification of Lombok Songket and Sasambo Batik Motifs Using the Convolution Neural Network (CNN) Algorithm

Sasambo batik is a traditional batik from the West Nusa Tenggara province. Sasambo itself is an abbreviation of three tribes, namely the Sasak (sa) in the Lombok Islands, the Samawa (sam), and the Mbojo (bo) tribes in Sumbawa Island. Classification of batik motifs can use image processing technology, one of which is the Convolution Neural Network (CNN) algorithm. Before entering the classification process, the batik image first undergoes image resizing. After that, proceed with the operation of the convolution, pooling, and fully connected layers. The sample image of Lombok songket motifs and Sasambo batik consists of 20 songket fabric data with the same motif and color and 14 songket data with the same motif but different colors. In addition, there are 10 data points on songket fabrics with other motifs and colors. In addition, there are 5 data points on Sasambo batik fabrics with the same motif and color and 5 data points on Sasambo batik fabrics with the same motif but different colors. The training data rotates the image by 150as many as 20 photos. Testing with motifs with the same color shows that the system's success rate is 83.85%. The highest average recognition for Sasambo batik cloth is in testing motifs with the same color for data in the database at 93.66%. The CNN modeling classification results indicate that the Sasambo batik cloth can be a reference for developing songket categorization using a website platform or the Android system.

Performance Comparison Between LeNet And MobileNet In Convolutional Neural Network for Lampung Batik Image Identification

Purpose: The rich cultural heritage of Indonesia includes the intricate art of batik, which varies across regions with unique patterns and motifs. This study focuses on Lampung batik, a distinctive type of batik, representing Lampung Province, Indonesia. Leveraging Convolutional Neural Network (CNN) architectures, namely LeNet-5 and MobileNet, the research compares their effectiveness in recognizing and classifying Lampung batik motifs. Data augmentation techniques, including rotation, brightness, and zoom, were employed to enhance the dataset and improve model performance.Methods: The study collected 500 Lampung batik images categorized into 10 classes which were then augmented and divided into training, validation, and testing sets. The model was created using a Deep Learning approach, LeNet And MobileNet. Both models were trained using identical hyperparameters and evaluated based on their accuracy in classifying Lampung batik motifs.Results: The results demonstrate an accuracy of 99.33% for LeNet-5 and 98.00% for MobileNet, outperforming previous studies. LeNet-5, particularly with augmentation, exhibited superior precision and recall in classifying Lampung batik motifs. This research underscores the efficacy of CNN architectures, coupled with data augmentation techniques, in accurately identifying intricate cultural artifacts like Lampung batik.Novelty: The Dharmagita learning model using a mobile application is a new model that has not existed before.

Yogyakarta Batik Image Classification Based on Convolutional Neural Network

This paper studies the efficiency of identifying motifs and patterns in Yogyakarta batik using the Convolutional Neural Network (CNN) algorithm. This research uses the AlexNet architecture on CNN to increase the accuracy of batik image classification. Apart from that, it also involves the use of Canny edge detection techniques and feature extraction using the Gray Level Co-occurrence Matrix (GLCM) to improve the feature extraction process in batik images. There are 6 folders representing 6 types of motifs containing -+20 to 25 data that have been prepared for the training session. Next, the data is processed with 20% of the data used for training and 80% for testing. The accuracy of this research using the SGDM optimizer reached 100%. The evaluation results provide insight into the extent to which edge emphasis can improve the model's ability to recognize and classify batik patterns. It also presents classification test results and evaluation metrics such as precision, recall, and F1 score.

Klasifikasi Batik Pekalongan Berdasarkan Citra dengan Metode GLCM dan JST Backpropagation

Batik is an Indonesian cultural heritage that is internationally recognized by UNESCO. However, knowledge about the types of batik, especially traditional Pekalongan batik, is increasingly forgotten due to globalization. This research aims to create a Pekalongan traditional batik image classification system through Gray Level Co-Occurrence Matrix (GLCM) feature extraction and Artificial Neural Network (ANN) classification method. This system aims to make it easier for people to identify Pekalongan batik motifs without requiring special skills. The results showed that the GLCM and JST methods can be used to classify Pekalongan batik can predict correctly. The use of JST Backpropagation architecture with 3 hidden layers resulted in train data accuracy of 46.6% and test data accuracy of 55.5%. This system is expected to help preserve the cultural heritage of batik and increase public understanding of Pekalongan batik motifs.

Klasifikasi Batik Parang Menggunakan Convolutional Neural Network (CNN)

Batik culture is very strong in Indonesia, this is the reason that batik can be found throughout the archipelago, with unique characteristics that distinguish it in each region. However, people are often confused and find it difficult to recognize one type of batik from another. One of the famous types of batik motif is Batik Parang. This research aims to establish a Convolutional Neural Network (CNN) model to classify Batik Parang and help people distinguish it from other batik motifs. Deep learning, particularly CNN, was chosen because it has a high accuracy rate in image classification. A quantitative Experimental design is used, using a dataset of 100 batik images evenly divided into two classes, namely Batik Parang and not Batik Parang. The dataset is divided into two categories, namely training data and testing data, with a data ratio of 80:20. Thus, by using Convolutional Neural Network (CNN), the classification between Batik Parang and not Batik Parang produces an accuracy of 95%, with the use of epoch = 118 and batch_size = 100.

Redesain Visual Identity Azyanu Batik

Azyanu Batik is an MSME engaged in the field of batik art based on local Minangkabau culture with "1000 Rumah Gadang" batik motifs which was founded by Zulmi Aryani since 2021, having its address at Muara Labuh, Solok Selatan Regency, West Sumatra Province. In this case, Azyanu Batik already has a visual identity but has not applied design principles and logo criteria to the fullest. The purpose of this design is to redesign Azyanu Batik's visual identity by applying design principles and good logo criteria as an effort to increase brand awareness and Azyanu Batik's image. The design method used is the 4D method, consisting of 4 stages, namely Define, Design, Develop, and Disseminate. While the design analysis used is through SWOT analysis. Azyanu Batik's visual identity redesign will be implemented into the main media and applicable media that can support the effectiveness of the visual identity created.

PENGENALAN MOTIF BATIK PESISIR PULAU JAWA MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

Coastal Batik is made outside of Solo and Yogyakarta. The use of the term "coastal" is due to the majority of batik production being located in the northern coast of Java, such as Indramayu, Cirebon, Pekalongan, Lasem, and others. Coastal batik is characterized by flexible color selection and patterns, influenced by foreign influences, particularly after the introduction of Islam in the 16th century. The Convolutional Neural Network (CNN) method is commonly used in classifying digital image data. Neurons in CNN are represented in a two-dimensional form, differing in linear function and weight parameters. The CNN extraction process consists of hidden layers, including convolutional, pooling, and ReLU (activation function) layers. The evaluation results of the Convolutional Neural Network model show that it can perform classification and recognize coastal batik images of Java Island, achieving the best results in the first scenario with a training data ratio of 70% and testing data ratio of 30%, resulting in an accuracy of 83%. For future research, it is recommended to increase the number of batik images and capture them directly, while incorporating segmentation or extraction features to measure efficiency and accuracy levels. This will help obtain better results in recognizing the characteristics of coastal batik in Java Island.

LSB Steganography to Embed Creator's Watermark in Batik Digital Arts

This paper presents an implementation of Least Significant Bit (LSB) steganography to embed a hidden watermark in batik digital images. The goal of the technique is to provide a means of protecting the intellectual property of creators of batik digital arts while still allowing the images to be freely distributed and shared. The study demonstrates the effectiveness of the technique by showing that the embedded watermark is not visible to the human eye and does not significantly alter the quality of the image. The proposed technique uses the LSB method to embed a watermark in the binary data of the batik digital image. This method involves replacing the least significant bit of the image's pixel values with the bits of the watermark, thus altering the binary data of the image without causing noticeable changes in its visual appearance. The embedded watermark can only be detected by a decoding process, making it difficult to be removed or tampered with. The results of the study show that the implemented LSB steganography technique is successful in achieving its objective. The technique is able to effectively embed a hidden watermark in batik digital images without significantly altering the image quality or causing visual artifacts. This work highlights the potential of LSB steganography as a valuable tool for protecting the intellectual property of digital art creators, particularly in the field of batik images. Overall, this study contributes to the growing body of research on steganography and digital media protection. The successful implementation of the LSB steganography technique provides a promising approach for safeguarding the intellectual property of creators in the digital arts industry. Future research may explore other steganography techniques to address potential vulnerabilities and limitations of the LSB method.

Batik Images Retrieval Using Pre-trained model and K-Nearest Neighbor

Batik is an Indonesian cultural heritage that should be preserved. Over time, many batik motifs have sprung up, which can lead to mutual claims between craftsmen. Therefore, it is necessary to create a system to measure the similarity of a batik motif. This research is focused on making Content-Based Image Retrieval (CBIR) on batik images. The dataset used in this research is big data Batik images. The authors used transfer learning on several pre-trained models and used Convolutional Neural Network (CNN) Autoencoder from previous studies to extract features on all images in the database. The extracted features calculate the Euclidean distance between the query and all images in the database to retrieve images. The image closest to the query will be retrieved according to the number of r, namely 3, 5, 10, or 15. Before the image is retrieved, the retrieval system is used to re-ranked with K-Nearest Neighbor (KNN), which classifies the retrieved image. The results of this study prove that MobileNetV2 + KNN is the best model in terms of Image Retrieval Batik, followed by InceptionV3 and VGG19 as the second and third ranks. Moreover, CNN Autoencoder from previous research and InceptionResNetV2 are ranked fourth and fifth. In this study, it was also found that the use of KNN re-ranking can increase the precision value by 0.00272. For further research, deploying these models, especially for MobileNetV2 is an approach for seeing a major impact on batik craftsmanship for decreasing batik motif plagiarism.

Generation of Batik Patterns Using Generative Adversarial Network with Content Loss Weighting

Every craftsman who draws Batik can also not necessarily draw various types of Batik. On the other hand, it takes a long time ranging from weeks to months, to make Batik. Image generation is regarded as an essential part of the field of computer vision. One of the popular methods includes the Generative Adversarial Network, commonly implemented to generate a new data set from an existing one. One model of the Generative Adversarial Network is BatikGAN SL generating batik images by inserting the two Batik patterns to produce a new Batik image. Currently, the generated Batik image does not maintain the input of the Batik pattern. Therefore, this study proposes a GAN model of BatikGAN SL, with the addition of a content loss function using hyperparameters to weight the content loss function. The content loss function is added from the Neural Transfer Style method. Previously, the style loss function in this method has been implemented in BatikGAN SL, and the dataset consists of Batik patches (326 images) and real Batik (163 images). This paper compares the BatikGAN SL model from previous studies with the BatikGAN SL model by implementing hyperparameters on the content loss function. The evaluation is conducted with FID, containing FID Local and FID Global. The results obtained in this study include a collection of Batik images, test evaluation value of 42 on FID Global and 16 on FID Local. These results are obtained by implementing the content loss function with a weight value of 1.

Coastal Batik Motifs Identification Using K-Nearest Neighbor Based on The Grey Level Co-occurrence Method

Indonesia is a country rich in natural, cultural, and tourism resources. One of the famous human cultural heritage in Indonesia is batik. Batik has unique motifs that are very diverse so it is difficult to recognize the in certain classes. This research was conducted to classify coastal batik, especially Tegal batik, Pekalongan batik, and Cirebon batik so that it can help facilitate the introduction and understanding of coastal batik when compared to another batik, such as Yogyakarta batik. The method used is Grey Level Co-occurrence Matrices to extract texture features, while, to determine the proximity of the test image to the training data using the K-Nearest Neighbor method, the calculation of the distance to be used is the Euclidean Distance and Manhattan Distance based on the texture characteristics of the batik image obtained. In this study, the highest score was obtained at 64% for Euclidean Distance and 66% for Manhattan Distance at k=15

Batik’s Pattern Recognition and Generation: Review and Challenges

Batik is one of cultural heritage acknowledged by UNESCO. Intelligence system comes as one of solution to take parts on preservation programs of this heritage. This study explores the current state of the art in application of artificial intelligence on Batik images. This research provides a systematic investigation and present the current progress and hot issues in recognition and generation area for Batik images. Furthermore, this research also presents several Batik data sets and their state of the art. As a result of the review, we are projecting several future works in the discussion.

Analysis of color features performance using support vector machine with multi-kernel for batik classification

Batik is a sort of cultural heritage fabric that originated in many areas of Indonesia. It can be traced back to many different parts of Indonesia. Each region, particularly Semarang in Central Java, Indonesia, has its Batik design. Unfortunately, due to a lack of knowledge, not all residents can recognize the types of Semarang batik. Therefore, this study proposed an automated method for classifying Semarang batik. Semarang batik was classified into five categories according to this method: Asem Arang, Blekok Warak, Gambang Semarangan, Kembang Sepatu, and Semarangan. It is required to analyze the color features based on the color space to develop discriminative features since color was able to differentiate these batik patterns. Color features were produced based on the RGB, HSV, YIQ, and YCbCr color spaces. Four different kernels were used to feed these features into the Support Vector Machine (SVM) classifier: linear, polynomial, sigmoid, and radial basis functions. The experiment was carried out using a local dataset of 1000 batik images classified into five classes (each class contains 200 images). A cross-validation test with a k-fold value of 10 was performed to analyze the method. In each of the SVM Kernels, the results showed that the proposed method achieved an accuracy value of 100% by utilizing the YIQ color space, which was reliable throughout all the tests.

Optimasi Metode K-Nearest Neighbor Dengan Particle Swarm Optimization Untuk Pengenalan Citra Batik Dengan Ragam Hias Geometris

Batik is an intangible cultural heritage that has been approved by UNESCO as an Indonesian cultural heritage. Batik is the art of drawing on cloth for clothing. This art of drawing is in the form of a pattern and has a philosophical meaning which is historically very closely related to the philosophy of Javanese culture. The various patterns of this batik need to be preserved, and one of the efforts to preserve this diversity is to maintain the special characteristics of the batik motif and continue to introduce it to future generations. Efforts to facilitate the introduction of batik motifs can be done through technology that is able to automatically recognize batik patterns according to the motif being tested, one of which is pattern recognition technology. This study aims to optimize the KNN method with PSO to increase the accuracy of batik pattern recognition, the research was carried out in several stages starting from data collection, preprocessing, feature extraction, and classification. The research was conducted using 310 data in the form of batik images in 7 different motifs and divided into 240 compositions for training data and 70 for testing data. At the feature extraction stage, the discrete wavelet transform method is used up to level 3, then at the classification stage, the PSO and KNN algorithms are used. The PSO algorithm is used to obtain the most optimal number of k which is used as the input parameter k in the KNN algorithm. The results of the research that have been carried out have proven that with the addition of the PSO algorithm, the accuracy of the KNN method can be increased by 6% compared to the standard KNN method.

Spatial and topology feature extraction on batik pattern recognition: a review

. Batik is an Indonesian cultural heritage that has been recognized by UNESCO as an international cultural heritage on October 2, 2009. Patterns of batik produce geometric shapes unique, the number and name of the batik patterns make it difficult to recognize each motif. The objective classification of batik is split image into classes according to the pattern motif motive so easy to recognize in accordance with its feature. Batik can be classified based on the shape of the motive, namely geometric motifs, geometric motifs and motifs non specific. Spatial information is an important aspect of image processing such as computer vision and recognition structure / pattern in the context of modelling and resolution of the uncertainty caused by the ambiguity in the low-level features. Shortcomings inherent in combining two colours and spatial features are not adaptive pattern recognition process of the region across multiple images and histogram matching is not appropriate to capture the colours on the image content. This study discussed a model of spatial features and feature combinations topology with the aim to improve the validation batik image pattern recognition so that the level of the pattern recognition motif batik image could be better. Some of the features that have been used include colour features and spatial features. In addition, this paper discusses the possibility of combining the features in pattern recognition. This paper proposes a combination of features that will be able to improve the validation of image pattern recognition of batik.

Klasifikasi Citra Batik Menggunakan Co-Occurrence Matrices Berbasis Wavelet Filter

<em><span lang="IN">Batik is the result of cultural arts that contains a philosophical meaning in each of its motifs. Various types of batik motifs create complexity in the recognition of batik image patterns. Classification of images into certain classes is also a problem in the field of pattern recognition. Machine learning is a method that is very developed at this time. Machine learning method is used to identify batik motifs through batik image classification. This study focuses on the image dataset of written batik which has two motifs, namely classical motifs and contemporary motifs. This study shows the experimental results of batik image classification using the Backpropagation Neural Network, Support Vector Machine and k-Nearest Neighbor classification methods. Co-occurrence matrices as wavelet filter-based feature extraction are used for input into batik image classification. The experimental results show that k-NN gets the best accuracy value of 95.56% while BPNN gets an accuracy value of 85.40% and SVM gets an accuracy value of 76.51%. Based on these results, it can be concluded that k-NN is the best method for classifying batik images with co-occurrence matrices as wavelet filter-based feature extraction.</span></em>

Klasifikasi Motif Citra Batik Menggunakan Convolutional Neural Network Berdasarkan K-means Clustering

Batik has several motifs and patterns so it is necessary to identify certain objects in an image, one of which is the recognition of the image of Yogyakarta batik using the Convolutional Neural Network (CNN) method which is already popular in the use of image data classification. The introduction of batik imagery aims to contribute to the digitization of batik image data and at the same time provide information on types of batik to the public. The batik image recognition process using CNN in this study combines the image segmentation process and the enhancement process with median filters and sharpening. The segmentation process carried out before CNN aims to help separate foreground objects from objects that are not needed in the background. The segmentation process that is commonly used is using K-means Clustering. Where K-means Clustering is used to group data in the same category. Furthermore, the enhancement process using the median filter and sharpening was carried out separately to compare the batik image classification process using CNN based on K-means Clustering from the median filter results and the sharpening results. The batik image classification process with CNN based on K-means Clustering on the median filter resulted in an accuracy value of 100%. Meanwhile, the batik image classification process with CNN based on K-means Clustering from the sharpening results resulted in an accuracy value of 80%.

Solo City Batik Design Security System (SiKemTi Solo) During the Pandemic Covid-19

Safeguarding copyright on traditional batik works is very important to prevent duplication of Indonesian cultural products. The ease of duplicating, especially in batik designs, causes frequent violations of the design's copyright. Especially with the rise of online commerce today, it is easier for everyone to use other people's products and copy other people's products. This will not happen if there is already a system that can be used to secure the design work. For this reason, it is important to develop methods that can be used to secure traditional batik works, especially Surakarta. Based on these problems, a research was conducted on the application of image watermarking techniques that are resistant to changes generated by image processing in the form of compression. The method used to secure Surakarta batik works is compression using wavelet transformation. The aim is to develop a batik design security system method using the watermarking method. The steps taken started with taking photos of batik designs and the designer's name, photos in the form of images followed by the embedding process using the watermarking method. The watermarking method used is the DWT method. After encryption, identification is carried out to determine the level of errors that occur. The results of testing 323 batik image data in this study obtained an average mse level = 0.00000065 and an average psnr result = 188.471186102179. From the results of this study, it was found that the development of methods that can be used to secure batik products by using the watermarking method.