Combining Local and Global Descriptors Through Rotation Invariant Texture Analysis for Ulos Classification

Panggabean, Teamsar Muliadi; Barus, Arlinta Christy

doi:10.1109/ritapp.2019.8932823

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…The Toba Batak are a tribe found throughout North Sumatra [1]. In everyday life, the Batak tribe maintains culture, as evidenced by the performance of traditional events with a distinct feature, namely, ulos [2].…”

Section: Introductionmentioning

confidence: 99%

An Effectivity Deep Learning Optimization Model to Traditional Batak Culture Ulos Classification

Muliono¹,

Iranita²,

Syah³

2023

IJACSA

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Ulos is one of the Batak culture's traditional heritage fabrics. Ulos cloth is divided into several types, each with a distinct function. Ulos Ragi Hotang, Ulos Pinunsaan, Ulos Tumtuman, Ulos Ragi Hidup, and Ulos Sadum are the five Batak ulos motifs. The Batak ulos motif has evolved over time and is now well-known in other countries. However, many ordinary people have difficulty distinguishing between ulos cloth and other fabrics. This study categorizes the different types of ulos cloth so that it can be used by ordinary people who are unfamiliar with the different types and functions. The Convolutional Neural Network is the method used (CNN). CNN is used to recognize and classify images. CNN's main feature is that it detects feature patches from locations in the input matrix and assembles them into high-level references. The Modular Neural Network (MNN) is then used to break down large and complex computational processes into smaller components, reducing complexity while still producing the desired output. 80% of the data for the training process, 20% for testing. The accuracy value achieved is 97.83%, the loss value is 0.0793, the val loss is 2.1885, and the val accuracy is 0.7429.

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Section: Introductionmentioning

confidence: 99%

An Effectivity Deep Learning Optimization Model to Traditional Batak Culture Ulos Classification

Muliono¹,

Iranita²,

Syah³

2023

IJACSA

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Classification of Weaving Motifs Based on Their Area of Origin Using the Support Vector Machine Algorithm

Jusman,

Tawaqal,

Intan Rahmawati

2024

E3S Web Conf.

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Indonesia has many cultural riches in the form of traditional fabrics, one of which is woven fabrics. Woven fabrics from each region showcase distinctive motifs, manifesting the local community’s daily life, culture, natural conditions, and beliefs. The diverse weaving motifs pose a challenge in determining the origin of the woven fabrics. It highlights the necessity of a system to detect and identify woven fabrics. Texture analysis was performed using the Gray Level Co-occurrence Matrix (GLCM). A classification method based on a Support Vector Machine (SVM) consisting of four models: Linear SVM, Quadratic SVM, Cubic SVM, and Fine Gaussian SVM was developed in this research. The images of woven fabrics came from three regions in Indonesia: Sumatra, Kalimantan, and Nusa Tenggara. This research utilized 240 training images and 12 testing images. The testing results unveiled that the Cubic SVM model, which achieved a 100% accuracy rate in 1.0835s, was the optimum SVM model for the weaving classification.

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Hand-Woven Fabric Motif Recognition Methods: A Systematic Literature Review

Noprisson

Ermatita

Abdiansah

et al. 2021

2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS

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Combining Local and Global Descriptors Through Rotation Invariant Texture Analysis for Ulos Classification

Cited by 3 publications

References 20 publications

An Effectivity Deep Learning Optimization Model to Traditional Batak Culture Ulos Classification

An Effectivity Deep Learning Optimization Model to Traditional Batak Culture Ulos Classification

Classification of Weaving Motifs Based on Their Area of Origin Using the Support Vector Machine Algorithm

Hand-Woven Fabric Motif Recognition Methods: A Systematic Literature Review

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