Efficient Pattern Recognition of Sundanese Script Variants Using CNN
Abstract
This research aims to apply pattern recognition technology, specifically through the Convolutional Neural Network (CNN) approach, in identifying and translating Sundanese script accurately. This research is focused on recognizing rarangken script patterns based on ngalagena script in Indonesian cultural heritage. This study uses the MobileNetV2 based CNN model, utilizing transfer learning and trained for 50 epochs using the Adam optimizer with a learning rate of 0.0001, to achieve a training accuracy of 98.75% and test accuracy of 96.95% in 1 hour and 23 minutes, respectively. The results of the study show that the simpler CNN architecture without augmentation achieved the highest accuracy of 99.26%, and the augmented CNN model achieved 94.42% accuracy in 2 hours and 22 minutes. These results enable practical applications in both education and cultural preservation, demonstrating how modern technology can effectively contribute to maintaining traditional cultural elements in the digital era.
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