Classification of Riau Batik Motifs Using the Convolutional Neural Network (CNN) Algorithm - Dhea Amanda Ramadhan; Dian Ramadhani (International Journal of Electrical, Energy and Power System Engineering)
Riau Batik, a treasured cultural heritage, faces challenges in its preservation due to limited public awareness of its unique motifs. This research aims to bridge the knowledge gap by developing a website-based classification system that can identify and recognize Riau batik patterns, offering round-the-clock accessibility to users. By leveraging the Convolutional Neural Network (CNN) algorithm, the classification system was trained using a dataset of 1,440 images. The model was fine-tuned through optimization of batch size and epoch parameters to maximize classification accuracy. The training process culminated in a model with an accuracy of 89%, achieved using a batch size of 16 and 50 epochs. This system seeks to elevate public appreciation and knowledge of Riau Batik, thereby contributing to the preservation of its cultural and historical significance. The accessible classification tool presents a practical approach to ensuring the motifs and legacy of Riau Batik are preserved for future generations. The proposed CNN-based model demonstrates the potential to enhance digital engagement with traditional culture through modern technology, facilitating widespread recognition and appreciation of Riau's rich batik heritage.