<![CDATA[This study introduces an advanced system for classifying Indonesian Rupiah (IDR) banknotes using Convolutional Neural Networks (CNNs). By harnessing sophisticated image processing techniques, the system analyzes surface patterns on banknotes and categorizes them based on distinct visual features to determine their denominations. The research utilizes a comprehensive dataset containing 1190 images across various IDR denominations, captured at a standardized resolution of 224x224 pixels. Prior to model training, meticulous data preprocessing techniques such as resizing, augmentation, and normalization are employed to optimize dataset quality and enhance model robustness. The CNN architecture is specifically designed with convolutional and pooling layers to automatically extract intricate features from banknote images, culminating in a softmax output layer for precise classification. Training the model through multiple epochs ranging from 50 to 150 showcases notable enhancements in accuracy, achieving up to 100% accuracy with minimal loss, indicative of its reliable performance. This research underscores the effectiveness of CNNs in automating banknote classification tasks, promising improved accuracy and efficiency in real-world applications.]]>
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