CLASSIFICATION OF LOMBOK SONGKET CLOTH IMAGE USING CONVOLUTION NEURAL NETWORK METHOD (CNN)

  • Hambali Hambali Universitas Teknologi Mataram
  • Mahayadi Mahayadi
  • Bahtiar Imran
DOI: https://doi.org/10.33480/pilar.v17i2.2705
Keywords: Histogram Equalization, Convolution Neural Network, Songket Cloth

Abstract

The diversity of tribes makes Indonesia rich in culture that characterizes it, one of which is traditional cloth. Through a variety of patterns and motifs that exist in traditional fabrics, reflecting the life, customs, and culture that exist in an area. Lombok is one of the areas that produces a typical songket cloth. The famous songket craft centers in Lombok are located in the Pringgasela area, Pringgasela District, Sade Village is in Pujut District, Central Lombok Regency and Sukarara is in Jonggat District, Central Lombok Regency. Each area of ​​the center for songket craftsmen has their own characteristics both in terms of the name, motif and texture. When viewed with the naked eye, the texture of each songket will look the same, to be able to know the differences in the texture of each songket, it is necessary to do a classification using computers or technology. Today's society still does not know much information about the textures of songket cloth. The method used to classify the typical Lombok songket in this study uses the Convolution Neural Network (CNN) method. The results obtained from the use of 64 datasets, with details of 40 types of Sade songket and 24 types of Pringgasela songket, after the dataset is trained it produces 86.36% accuracy, 87% precision, 86% recall, and 86% F1-Score.

 

Keywords: Histogram Equalization, Convolution Neural Network, Songket Cloth.

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Published
2021-09-08
How to Cite
Hambali, H., Mahayadi, M., & Imran, B. (2021). CLASSIFICATION OF LOMBOK SONGKET CLOTH IMAGE USING CONVOLUTION NEURAL NETWORK METHOD (CNN). Jurnal Pilar Nusa Mandiri, 17(2), 149-156. https://doi.org/10.33480/pilar.v17i2.2705
Issue
Vol 17 No 2 (2021): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Period for September 2021
Section
Articles

Copyright (c) 2021 Hambali Hambali, Mahayadi Mahayadi, Bahtiar Imran

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