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All Journal INFORMATIKA
Witriyani Witriyani
STMIK IKMI Cirebon
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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ADAPTIVE CLASS WEIGHTING DAN AUGMENTATION UNTUK KLASIFIKASI BATIK KERATON Witriyani Witriyani; Dian Ade Kurnia; Yudhistira Arie Wijaya; Mulyawan Mulyawan; Irfan Ali
Informatika: Jurnal Teknik Informatika dan Multimedia Vol. 6 No. 1 (2026): MEI : JURNAL INFORMATIKA DAN MULTIMEDIA
Publisher : LPPM Politeknik Pratama Kendal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/informatika.v6i1.1516

Abstract

This study aims to improve the performance of Batik Keraton motif classification on an imbalanced dataset through the integration of adaptive class weighting and data augmentation within a transfer learning framework. The dataset consists of 1,799 images across four classes (Kawung, Mega Mendung, Parang, Truntum), preprocessed to 224×224 pixels and split stratifiedly into training, validation, and test sets (80/10/10). Three transfer learning architectures—ResNet50V2, VGG16, and EfficientNetB0—were evaluated with adaptive class weighting and geometric augmentation to enhance minority-class representation. The results indicate that ResNet50V2 with pretrained weights achieved the best performance, reaching a test accuracy of 92.78%, macro precision of 93.13%, macro recall of 92.79%, and a macro F1-score of 92.83%. Adaptive class weighting improved sensitivity toward minority classes, while augmentation contributed to model stability and generalization. These findings demonstrate that combining adaptive weighting and augmentation effectively enhances Batik Keraton motif classification under imbalanced data conditions.  
Co-Authors Dian Ade Kurnia Irfan Ali Mulyawan Yudhistira Arie Wijaya