Classification of Toraja Wood Carving Motif Images Using Convolutional Neural Network (CNN)

Nurilmiyanti Wardhani; Billy Eden William Asrul; Antonius Riman Tampang; Sitti Zuhriyah; Abdul Latief Arda

doi:10.29207/resti.v8i4.5897

Nurilmiyanti Wardhani Universitas Handayani Makassar
Billy Eden William Asrul Universitas Handayani Makassar
Antonius Riman Tampang Universitas Handayani Makassar
Sitti Zuhriyah Universitas Handayani Makassar
Abdul Latief Arda Universitas Handayani Makassar

DOI: https://doi.org/10.29207/resti.v8i4.5897

Keywords: CNN, image classification, Toraja wood carvings

Abstract

Wood carving is a cultural heritage with deep meaning and significance for the Toraja ethnic group's culture. By understanding the meaning of each Toraja carving, both tourists and the local community can gain knowledge about Toraja culture, thereby preserving and maintaining the culture amidst modern developments. Image processing approaches, particularly the development of Convolutional Neural Networks (CNN), offer a solution for extracting information from the diverse and intricate patterns of Toraja wood carvings. This study is highly significant as it implements a deep learning model using the CNN algorithm optimized with the ResNet50 architecture. The methodology in this study involves adjusting the batch size during the model training phase and applying weak-to-strong pixel transformation during the double threshold hysteresis phase in the Canny Feature Extraction process on the edges of Toraja carving images, resulting in ResNet50 architecture accurately recognizing the patterns of Toraja wood carvings. The results demonstrate significant improvements in the performance of the ResNet50 architecture with the preprocessed dataset. average precision, recall, precision, and F1-Score improvements in each Toraja carving class. For the Pa' Lulun Pao class, it was found that the precision and recall values were 0.94, and the F1-Score was 0.94. The Pa’ Somba class also showed good results, with a precision value of 0.9697, a recall of 0.96, and an F1-Score of 0.9648. The Pa’ Tangke Lumu class showed even better results, with a precision value of 0.9898, a recall of 0.97, and an F1-Score of 0.9798. The Pa’ Tumuru class also demonstrated good performance, with a precision value of 0.9327, a recall of 0.97, and an F1-Score of 0.9500. This study not only underscores the effectiveness of processing in enhancing CNN capabilities but also opens opportunities for further research in applying these methods to various image types and exploring different CNN architectures.

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