Multi-architectural Transfer Learning CNN for Klowong Batik Fabric Defect Classification

Dhika Wahyu Pratama; Andi Sudiarso; Denny Sukma Eka  Atmaja; Muhammad Kusumawan  Herliansyah

doi:10.52436/1.jutif.2025.6.4.4806

Authors

Dhika Wahyu Pratama Department Mechanical and Industrial Engineering, Universitas Gadjah Mada, Indonesia
Andi Sudiarso Department Mechanical and Industrial Engineering, Universitas Gadjah Mada, Indonesia
Denny Sukma Eka Atmaja School of Industrial and Systems Engineering, Telkom University, Indonesia
Muhammad Kusumawan Herliansyah Department Mechanical and Industrial Engineering, Universitas Gadjah Mada, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.4.4806

Keywords:

Batik, Classification, CNN, Klowong

Abstract

Klowong is a base cloth that has been given a hot wax pattern as the initial stage in the batik making process but has not yet become a finished batik. Nowdays, written batik machine are available but still limited and production defects still occur, reducing the value of batik. Manual QC makes subjective assessments, so an accurate and efficient automated inspection system is needed for SMEs.This study proposes a defect classification approach on batik klowong fabric based on transfer learning using deep convolutional neural networks (CNN) architecture that has been verified to be reliable in image classification schemes. The basic models used include VGG16, ResNet50V2, InceptionV3, and MobileNetV2, with modifications to the fully connected layers to reduce parameter complexity. The dataset consists of 1000 klowong fabric images with a resolution of 224×224 pixels, with a ratio of 80:10:10 for training, validation, and testing. Data augmentation was applied to improve the generalization of the model. Evaluation is performed based on accuracy, precision, recall, F1-score, and inference time. The experimental results show that VGG16 has the best performance in the testing stage with 92% accuracy. The combination of VGG16 with conventional classifiers (SVM and Random Forest) significantly speeds up the inference time (up to 0.0001 seconds per image) but with a decrease in accuracy to 81-83%. Therefore, the VGG16 model with the modified final layer is recommended as the optimal solution with the best trade-off between classification performance and computational efficiency, especially for application scenarios on low-resource devices such as batik SMEs.

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