Modified Convolutional Neural Network Architecture for Batik Motif Image Classification

Wicaksono, Ardian Yusuf; Suciati, Nanik; Fatichah, Chastine; Uchimura, Keiichi; Koutaki, Gou

doi:10.12962/j23378530.v2i2.a2846

Cited by 35 publications

(15 citation statements)

References 18 publications

(17 reference statements)

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“…Classification of batik pattern have been done before [15][16][17]. In batik classification, the CNN method proved to be the best choice [15][16].…”

Section: Research Methods 21 Related Researchmentioning

confidence: 99%

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Expert role in image classification using CNN for hard to identify object: distinguishing batik and its imitation

Widyantoko¹,

Widowati²,

Isnaini³

et al. 2021

IJ-AI

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<span id="docs-internal-guid-25a2977b-7fff-96bd-b93a-19bd55e68ea7"><span>In this research we try to solve the recognition problem in differentiating between batik and its imitation. Batik is an Indonesian heritage of process in making traditional textile product that is now endangered by the existence of imitation products. We try to compare two popular CNN model to classify batik products into five classes. The classes are tulis, cap, print warna, print malam, cabut warna. Tulis and cap are genuine batik, and the other three are an imitation. We realize that this problem is go beyond the recognition of fine grained image problem, it is a hard to identify image problem because even the batik experts is having a hard time identifying batik and its imitation if only based on its picture. The two CNN models, inceptionV3 and mobilenetV2 were trained on three types of image. One type is a freely taken image, the other two were taken based on the experts suggestion. The accuracy score shows that the model trained with the suggestion based picture perform better than the one trained with the random picture.</span></span>

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“…Classification of batik pattern have been done before [15][16][17]. In batik classification, the CNN method proved to be the best choice [15][16].…”

Section: Research Methods 21 Related Researchmentioning

confidence: 99%

“…Classification of batik pattern have been done before [15][16][17]. In batik classification, the CNN method proved to be the best choice [15][16]. However all the work before focused on identifying the batik motif or the shape of the batik pattern.…”

Section: Research Methods 21 Related Researchmentioning

confidence: 99%

Expert role in image classification using CNN for hard to identify object: distinguishing batik and its imitation

Widyantoko¹,

Widowati²,

Isnaini³

et al. 2021

IJ-AI

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“…Penelitian terdahulu mengenai deep learning menggunakan convolutional neural network sudah banyak dilakukan oleh para peneliti pada berbagai macam objek. Seperti halnya penelitian yang dilakukan oleh peneliti sebelumnya yang menggunakan CNN guna mengklasifikasi motif batik [6].Dari penelitian sebelumnya yaitu pendeteksi jenis mangga di India dikatakan bahwa model CNN dapat mengklasifikasikan di antara berbagai jenis buah mangga dengan kinerja yang sangat memuaskan yaitu nilai AUCROC sebesar 97,3% [7].…”

Section: Metode Penelitianunclassified

Pendiagnosa Daun Mangga Dengan Model Convolutional Neural Network

Ayu

Dwi

Minarno

2021

Com, Engine, Sys, Sci

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Pertanian adalah salah satu sektor ekonomi yang terpenting di negara-negara Asia Tenggara. Saat ini, pembangunan ekonomi sangat bergantung pada pertanian. Seperti contoh Mangga, Manga juga merupakan bahan makanan yang dapat diolah menjadi berbagai jenis makanan yang lezat. Karena banyaknya manfaat pada buah ini tak jarang masyarakat ingin menanam pohon mangga untuk dibudidayakan dengan tujuan komersil maupun pribadi. Salah satu masalah utama yang menurunkan kualitas dan kuantitas manufaktur pertanian adalah penyakit tanaman. Oleh karena itu bidang penelitian pertanian menarik para peneliti dan ilmuwan untuk memberikan teknik untuk mengidentifikasi penyakit tanaman dengan menggunakan pengolahan gambar dan visi komputer seperti dalam kertas ini yang menggunaka model Convolutional Neural Network (CNN) untuk mengklasifikasi jenis daun mangga yang sakit (terserang hama) dan sehat berdasarkan bentuk dan tekstur daun. Pada penelitian yang dihasilkan tingkat akurasi sebesar 0,96.

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“…In ImageNet Large ScaleVisual Recognition Challenge (ILSVRC), the majority of the winners used CNN as the base [15] - [22]. Work conducted by [13] classified Indonesian batik by using CNN with new network architecture, which is a merging with GoogLeNet with Residual Network (IncRes). Work conducted by [14] used CNN method with VGG16architecture [23] in addressing the problems of classification of batik.…”

Section: Related Workmentioning

confidence: 99%

“…Related works on the classification of batik has been carried out lately.These works generally can be divided into two groups: (1)Classification using handcrafted features [1] - [12], and (2)Classification using deep learning approaches [13] - [14]. Along with its many new method developments, so in this work we will conduct an evaluation of several deep learning models.…”

Section: Introductionmentioning

confidence: 99%

Batik Classification using Deep Learning

Putra¹,

Kusuma*²

2019

IJRTE

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Batik is one of the Indonesian cultural heritages that has been recognized by the global community. Indonesian batik has a vast diversity in motifs that illustrate the philosophy of life, the ancestral heritage and also reflects the origin of batik itself. Because of the manybatik motifs, problems arise in determining the type of batik itself. Therefore, we need a classification method that can classify various batik motifs automatically based on the batik images. The technique of image classification that is used widely now is deep learning method. This technique has been proven of its capacity in identifying images in high accuracy. Architecture that is widely used for the image data analysis is Convolutional Neural Network (CNN) because this architecture is able to detect and recognize objects in an image. This workproposes to use the method of CNN and VGG architecture that have been modified to overcome the problems of classification of the batik motifs. Experiments of using 2.448 batik images from 5 classes of batik motifs showed that the proposed model has successfully achieved an accuracy of 96.30%.

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Modified Convolutional Neural Network Architecture for Batik Motif Image Classification

Cited by 35 publications

References 18 publications

Expert role in image classification using CNN for hard to identify object: distinguishing batik and its imitation

Expert role in image classification using CNN for hard to identify object: distinguishing batik and its imitation

Pendiagnosa Daun Mangga Dengan Model Convolutional Neural Network

Batik Classification using Deep Learning

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