| Abstrak/Abstract |
A content-based image retrieval system, as an Indonesian traditional woven fabric knowl-
edge base, can be useful for artisans and trade promotions. However, creating an effective and
efficient retrieval system is difficult due to the lack of an Indonesian traditional woven fabric dataset,
and unique characteristics are not considered simultaneously. One type of traditional Indonesian fab-
ric is ikat woven fabric. Thus, this study collected images of this traditional Indonesian woven fabric
to create the TenunIkatNet dataset. The dataset consists of 120 classes and 4800 images. The images
were captured perpendicularly, and the ikat woven fabrics were placed on different backgrounds,
hung, and worn on the body, according to the utilization patterns. The feature extraction method
using a modified convolutional neural network (MCNN) learns the unique features of Indonesian
traditional woven fabrics. The experimental results show that the modified CNN model outperforms
other pretrained CNN models (i.e., ResNet101, VGG16, DenseNet201, InceptionV3, MobileNetV2,
Xception, and InceptionResNetV2) in top-5, top-10, top-20, and top-50 accuracies with scores of
99.96%, 99.88%, 99.50%, and 97.60%, respectively. |
