Implementation of Data Augmentation Using Convolutional Neural Network for Batik Classification

Khasanah, Chan Uswatun; Utami, Ema; Raharjo, Suwanto

doi:10.1109/citsm50537.2020.9268890

Cited by 8 publications

(3 citation statements)

References 24 publications

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“…In addition, data augmentation was used in this study to diversify data variations. A process called "data augmentation" involves manipulating the rotation, brightness, cropping, and reversal of images [13,14].…”

Section: Pre-processingmentioning

confidence: 99%

Comparison of Machine Learning Performance Using Naive Bayes and Random Forest Methods to Classify Batik Fabric Patterns

Fadlil¹,

Riadi²,

Putra³

2023

RIA

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Batik is a work of art from Indonesia that has many types and pattern. One of the batik producing areas is Surakarta, the famous pattern in this area are Sawat, Sementrante, and Satriomanah. The problem that arises is the difficulty of distinguishing the three existing pattern because they have a high level of similarity. Therefore, this research aims to solve these problems using NB and RF methods. As a feature extraction, a Gray Level Cooccurrence Matrix is used as a texture feature extraction. The research phase includes methods for dataset collection, preprocessing, feature extraction, and classification. These two methods, RF and NB, can be used as methods for batik fabric classification. The most accurate result obtained by the RF method was 97.91% accurate in dataset A, while the NB method was 96.66% accurate on the same dataset. According to the research results, it is found that the RF method outperforms the NB method in classifying the types of batik patterns.

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Section: Pre-processingmentioning

confidence: 99%

Comparison of Machine Learning Performance Using Naive Bayes and Random Forest Methods to Classify Batik Fabric Patterns

Fadlil¹,

Riadi²,

Putra³

2023

RIA

View full text Add to dashboard Cite

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“…By choosing data augmentation, batik classification accuracy was successfully raised by 3.13%, from 95.83% (without data augmentation) to 98.96%. (by selecting data augmentation) [13]. Furthermore, there is research by Rasyidi et al In this study, the six batik patterns Banji, Ceplok, Kawung, Mega Mendung, Parang, and Sekar Jagad were identified using the convolutional neural network (CNN).…”

Section: Introductionmentioning

confidence: 98%

Image Classification On Garutan Batik Using Convolutional Neural Network with Data Augmentation

Fitriani

Tresnawati

Sukriyansah

2023

JUITA

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In Indonesia, Batik is one of the cultural assets in the field of textiles with various styles. There are many types of batik in Indonesia, one of which is Batik Garutan. Batik Garutan has different motifs that show the characteristics of Batik Garutan itself. Therefore, to distinguish the features of Batik Garutan from another batik, a system is needed to classify the types of batik patterns. Classification of batik patterns can be done using image classification. In image classification, there are methods to increase the size and quality of the limited training dataset by performing data augmentation. This study aims to obtain an image classification model by applying data augmentation. The image classification process is carried out using the Deep Learning method with the Convolutional Neural Network algorithm, which is expected to be helpful as a reference for research and can be applied to software development related to image classification. This study generated models from several experiments with different epoch parameters and dataset proportions. A system obtained the investigation with the best performance with a data proportion of 9:1, resulting in an accuracy value of 91 percent.

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“…Convolutional Neural Network (CNN) is the most widely used approach in various studies for the task of detecting and classifying [21]. CNN has been successfully applied in various fields, including pattern recognition in batik motifs [22]- [24]. Deep learning CNN is considered a promising approach to cirrhosis detection due to its ability to outperform traditional methods and provide accurate predictions [25].…”

Section: Introductionmentioning

confidence: 99%

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

Andrian,

Herwanto,

Taufik

et al. 2024

SJI

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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.

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Implementation of Data Augmentation Using Convolutional Neural Network for Batik Classification

Cited by 8 publications

References 24 publications

Comparison of Machine Learning Performance Using Naive Bayes and Random Forest Methods to Classify Batik Fabric Patterns

Comparison of Machine Learning Performance Using Naive Bayes and Random Forest Methods to Classify Batik Fabric Patterns

Image Classification On Garutan Batik Using Convolutional Neural Network with Data Augmentation

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

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