Implementasi Deep Learning Berbasis Convolutional Neural Network untuk Klasifikasi Motif Tenun Timor
DOI:
https://doi.org/10.32938/jitu.v4i1.7971Keywords:
Deep Learning, Convolutional Neural Network, Timor WeavingAbstract
This research develops a classification model for Timorese weaving motifs, including Buna, Kaimafafa, Kemak, and Nunkolo motifs, using Deep Learning method based on Convolutional Neural Network (CNN). Timor's diverse weaving motifs reflect the richness of the local culture, but manual classification is often time-consuming. To overcome this challenge, we applied CNN with transfer learning techniques to a dataset of pre-processed Timorese weaving images. Based on the experimental results, the developed model achieved an accuracy of 95.00% on the test data with the use of 20 epochs, demonstrating the effectiveness of CNN in classifying weaving motifs automatically and efficiently. This research has the potential to support cultural preservation and the development of the weaving industry through technology-based practical applications that are optimal in terms of performance and computational efficiency.
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Copyright (c) 2024 Budiman Baso, Ramaulvi Muhammad Akhyar (Author)
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