Classification Of South Sumatra Songket Woven Fabric Motifs Using Deep Learning
DOI:
https://doi.org/10.61978/data.v2i3.313Keywords:
Deep Learning, Songket Motifs, Textile Classification, YOLO Algorithm, Traditional MarketsAbstract
The application of a Deep Learning model to classify songket woven cloth motifs from South Sumatra reflects the richness of local culture through its diverse motifs. The main challenge faced is the difficulty in distinguishing various songket motifs due to their complexity and wide variety of designs. This research aims to: (1) develop an effective Deep Learning model for classifying songket woven fabric motifs, (2) measure the accuracy and performance of the model, and (3) assess the implications of this model for cultural preservation and the textile industry. The research method employs the SEMMA (Sample, Explore, Modify, Model, and Assess) approach in the data mining process, which includes five phases: data sampling, data exploration, data modification, data modeling, and model evaluation. Songket motif image data is collected, processed, and modeled using the YOLO (You Only Look Once) algorithm for accurate predictions. Data analysis is conducted to assess the model's accuracy, precision, recall, and F1-score. The research results show that the developed system is capable of accurately classifying songket motifs, confirming the effectiveness of Deep Learning models in overcoming this challenge. These findings have significant implications for cultural preservation and textile industry applications, highlighting the potential of Deep Learning technology in processing and analyzing traditional textile data.
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