Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Aiti: Jurnal Teknologi Informasi
Santosa, Stefaron Budhi
Unknown Affiliation
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Klasifikasi motif Batik Keraton menggunakan arsitektur fine-tuning ResNet-50. Santosa, Stefaron Budhi; Chrismanto , Antonius Rachmat; 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. Researchers will also compare accuracy with other deep learning models for classifying Keraton Batik images. This study concludes that applying a fine-tuned ResNet50 model with additional CNN layers and data augmentation effectively classifies batik motifs, offering substantial potential to automate batik motif recognition and supports digital preservation and development of batik in the cultural and creative industries.
Co-Authors Budi Susanto Chrismanto , Antonius Rachmat