Identification of Batik in Central Java using Transfer Learning Method

Stephanie Pamela Adithama, B. Yudi Dwiandiyanta, Sevia Berliana Wiadji
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Abstract

Identification of Batik in Central Java using Transfer Learning Method. Batik was recognized as a human heritage for oral and nonmaterial culture by UNESCO due to its symbolic and philosophical ties to the lives of Indonesians. However, the younger generation is gradually losing itslegacy because of technological and sociological changes that have influenced Indonesian batik. Consequently, batik knowledge is disappearing. A convolutional neural network and transfer learning techniques were utilized in deep learning to construct a model recognising batik motifs. The study utilized a dataset of one thousand images, five classes of batik designs (Banji, Kawung, Slope, Parang, and Slobog), and pre-trained architectural models VGG16 and VGG19 on Keras. The best model utilizes the VGG16 architecture, and the number of epochs is 50,with the result of testing accuracy of 0.9200.
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用迁移学习方法识别中爪哇蜡染
用迁移学习方法识别中爪哇蜡染。蜡染被联合国教科文组织认定为口头和非物质文化的人类遗产,因为它与印度尼西亚人的生活有着象征性和哲学上的联系。然而,由于技术和社会的变化影响了印尼蜡染,年轻一代正在逐渐失去它的遗产。因此,蜡染知识正在消失。利用卷积神经网络和迁移学习技术在深度学习中构建蜡染图案识别模型。该研究使用了1000张图像的数据集,5类蜡染设计(Banji, Kawung, Slope, Parang和Slobog),以及Keras上预训练的建筑模型VGG16和VGG19。最佳模型采用VGG16架构,迭代次数为50次,测试精度为0.9200。
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