Classification of Batik Lamongan Based on Features of Color, Texture and Shape

Sholihin, Miftahus

doi:10.28961/kursor.v9i1.114

Cited by 7 publications

(6 citation statements)

References 10 publications

(13 reference statements)

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“…Klasifikasi motif batik merupakan pemrosesan gambar batik menjadi informasi yang dapat dipahami oleh komputer sehingga dapat dimanfaatkan untuk banyak tujuan, seperti memberi wawasan pengenalan motifmotif batik berbasis teknologi. Klasifikasi motif batik melibatkan beberapa tahapan, 2 dimulai dari preprocessing hingga proses pengenalan melalui algoritma klasifikasi (Nurhaida et al, 2016;Sholihin, 2018). Batik sendiri terbilang unik dan sulit untuk diidentifikasi karena memiliki motif beragam yang setiap motifnya mengandung unsur budaya yang berbeda-beda serta warna yang juga berbeda-beda (Andrian et al, 2019 (Agastya & Setyanto, 2018;Minarno et al, 2020).…”

Section: Pendahuluanunclassified

“…Terdapat penelitian yang dilakukan mengenai klasifikasi atau identifikasi batik. Seperti penelitian yang dilakukan (Fadlil et al, 2023), (Riadi et al, 2023) Penelitian lain yang dilakukan oleh (Sholihin, 2018), metode pemilihan fitur untuk klasifikasi gambar batik menjadi Kawung, Lereng, Nitik dan Tambal. Pemilihan fitur yang tepat dengan menghilangkan redundan fitur dapat menghasilkan akurasi yang lebih tinggi.…”

Section: Pendahuluanunclassified

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Klasifikasi Citra Batik Sumatera Menggunakan Naïve Bayes Berbasis Fitur Ekstraksi GLCM

Sinaga,

Jatmoko

2024

semnasristek

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Salah satu budaya Indonesia yang masih tetap bertahan hingga sekarang adalah batik. Hingga saat ini, ragam motif batik terus berkembang di nusantara seperti batik kawung, batik parang, batik sidomukti, dll. Banyaknya pola batik di Indonesia membuat identifikasi menjadi sulit dan membutuhkan suatu sistem yang dapat mengklasifikasikan motif batik. Pada penelitian ini akan menggunakan sistem dengan mengusulkan metode ekstraksi fitur tekstur menggunakan metode Gray Level Co-Occurrence Matix (GLCM) serta klasifikasi jenis motif batik dengan Naïve Bayes. Terdapat 15 motif Batik Sumatera yang digunakan dalam penelitian ini, di mana jenis batik ini meliputi daerah asal yaitu Aceh, Sumatra Utara, Sumatra Barat, Riau, Jambi, Bengkulu, Sumatera Selatan, Kepulauan Bangka Belitung dengan 100 dataset untuk setiap motifnya. Penelitian ini menggunakan 1500 dataset citra batik, dengan split data 70:30 sehingga 1050 citra digunakan sebagai Data Training dan 450 citra digunakan sebagai data testing. Parameter GLCM yang dipakai yaitu contrast, correlation, energy, entropi, dan homogeniti. Dari hasil percobaan diketahui bahwa Naïve Bayes menghasilkan akurasi hingga 96,66%.

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Section: Pendahuluanunclassified

Klasifikasi Citra Batik Sumatera Menggunakan Naïve Bayes Berbasis Fitur Ekstraksi GLCM

Sinaga,

Jatmoko

2024

semnasristek

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“…Work conducted by [3] classified Indonesian batik by extracting the texture features and shape using GLCMmethod, while the classification process was conducted by using Artificial Neural. Work by [4] conducted a study in which the classification process will be based on the extraction of the similarity of textures, shapes and colors. For the extraction process of the texture similarity, this work used GLCM method, for the similarity of colors, itwas usedthe color moment, for the similarity of shape,it was used the invariant moment, and the classification by using the K-NN.…”

Section: Related Workmentioning

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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“…GLCM is a feature recognition that has been used in various fields [16]- [19]. GLCM is one of the algorithms used for image recognition, including songket images [20], [21]. GLCM has good results in the Classification of batik motifs [22].…”

Section: Introductionmentioning

confidence: 99%

Application of Gray Scale Matrix Technique for Identification of Lombok Songket Patterns Based on Backpropagation Learning

Sasongko

Jayanti²,

Ariessaputra³

2022

JOIV : Int. J. Inform. Visualization

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Songket is a woven fabric created by prying the threads and adding more weft to create an embossed decorative pattern on a cotton or silk thread woven background. While songket from many places share similar motifs, when examined closely, the motifs of songket from various regions differ, one of which is in the Province of West Nusa Tenggara, namely Lombok Island. To assist the public in recognizing the many varieties of Lombok songket motifs, the researchers used digital image processing technology, including pattern recognition, to distinguish the distinctive patterns of Lombok songket. The Gray Level Co-occurrence Matrix (GLCM) technique and Backpropagation Neural Networks are used to build a pattern identification system to analyze the Lombok songket theme. Before beginning the feature extraction process, the RGB color image has converted to grayscale (grayscale), which is resized. Simultaneously, a Backpropagation Neural Network is employed to classify Lombok songket theme variations. This study used songket motif photos consisting of a sample of 15 songket motifs with the same color theme that was captured eight times, four of which were used as training data and kept in the database. Four additional photos were utilized as test data or data from sources other than the database. When the system’s ability to recognize the pattern of Lombok songket motifs is tested, the maximum average recognition percentage at a 0° angle is 88.33 percent. In comparison, the lowest average recognition percentage at a 90° angle is 68.33 percent.

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Classification of Batik Lamongan Based on Features of Color, Texture and Shape

Abstract: Batik is as one of

Cited by 7 publications

References 10 publications

Klasifikasi Citra Batik Sumatera Menggunakan Naïve Bayes Berbasis Fitur Ekstraksi GLCM

Klasifikasi Citra Batik Sumatera Menggunakan Naïve Bayes Berbasis Fitur Ekstraksi GLCM

Batik Classification using Deep Learning

Application of Gray Scale Matrix Technique for Identification of Lombok Songket Patterns Based on Backpropagation Learning

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