Implementation of the CNN Deep Learning Method in Tajong (Sarung) Samarinda Classification

Samarinda sarongs are one of Indonesia's traditional fabrics that are famous for their beautiful motifs and textures. This fabric is made using traditional weaving techniques using non-machine looms (ATBMs), resulting in a unique and distinctive diversity of textures. The difference between the loom, namely the machine and the non-machine, resulting in a difference in the texture of the Samarinda sarong. This difference can be seen from the thread density, texture smoothness, and sharpness of the motif. On certain Samarinda sarong motifs that do not require special details. This study aims to develop a classification model of Samarinda sarong texture based on the loom (machine and non-machine) using the Deep Learning method. This model is expected to help, increase the selling value of Samarinda sarongs, preserve and promote traditional fabrics In this context, the choice between DenseNet121 and VGG16 can depend on user preferences or specific needs, such as computing speed or model size.

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Publication Details

Journal
Journal of Applied Sciences Management and Engineering Technology
Published
2024-09-30
DOI
https://doi.org/10.31284/j.jasmet.2024.v5i2.6406
Primary Topic
Data Mining and Machine Learning Applications
Type
article
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article

Implementation of the CNN Deep Learning Method in Tajong (Sarung) Samarinda Classification

Sitti Muhartini, Andi Sunyoto, Alva Hendi Muhammad
Journal of Applied Sciences Management and Engineering Technology
Data Mining and Machine Learning Applications
article

Implementation of the CNN Deep Learning Method in Tajong (Sarung) Samarinda Classification

Sitti Muhartini, Andi Sunyoto, Alva Hendi Muhammad
article en

Abstract

Samarinda sarongs are one of Indonesia's traditional fabrics that are famous for their beautiful motifs and textures. This fabric is made using traditional weaving techniques using non-machine looms (ATBMs), resulting in a unique and distinctive diversity of textures. The difference between the loom, namely the machine and the non-machine, resulting in a difference in the texture of the Samarinda sarong. This difference can be seen from the thread density, texture smoothness, and sharpness of the motif. On certain Samarinda sarong motifs that do not require special details. This study aims to develop a classification model of Samarinda sarong texture based on the loom (machine and non-machine) using the Deep Learning method. This model is expected to help, increase the selling value of Samarinda sarongs, preserve and promote traditional fabrics In this context, the choice between DenseNet121 and VGG16 can depend on user preferences or specific needs, such as computing speed or model size.

Journal of Applied Sciences Management and Engineering TechnologyVol. 5(2)
Universitas Respati Yogyakarta (ID), Universitas Amikom Yogyakarta
Openalex Percentile: Top 69%
Data Mining and Machine Learning Applications
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Implementation of the CNN Deep Learning Method in Tajong (Sarung) Samarinda Classification — Sitti Muhartini, Andi Sunyoto, et al. · Journal of Applied Sciences Management and Engineering Technology (2024) | TGRS Research Map | TGRS