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All Journal Aiti: Jurnal Teknologi Informasi
Rachmat Chrismanto , Antonius
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Computer Science & IT

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Klasifikasi motif Batik Keraton menggunakan arsitektur fine-tuning ResNet-50 Budhi Santosa, Stefaron; Rachmat Chrismanto , Antonius; Susanto, Budi
AITI Vol 23 No 1 (2026)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v23i1.46-60

Abstract

Batik is an Indonesian cultural heritage known for its diverse motifs; however, manual classification of these motifs remains a significant challenge. This study aims to develop a batik motif classification model using the ResNet50 architecture enhanced with data augmentation to improve model accuracy. The dataset consists of four batik motif classes: Kawung, Mega Mendung, Parang, and Truntum. In this research, the model was trained using fine-tuning on ResNet50, with additional CNN layers for feature extraction. The results demonstrated that the proposed model achieved a highest accuracy of 97.80% on test data and 96.80% on validation data, significantly outperforming methods without data augmentation. This study concludes that applying fine-tuned ResNet50 with additional CNN layers and data augmentation effectively classifies batik motifs, offering substantial potential for automating the classification process in the batik industry.
Co-Authors Budhi Santosa, Stefaron Budi Susanto