<![CDATA[Traditional Indonesian spice identification has historically depended on human expertise, a process prone to subjective error and limited scalability. This study evaluates the use of an EfficientNet-B0-based Convolutional Neural Network, incorporating transfer learning and fine-tuning, to automatically classify digital images representing 31 categories of traditional Indonesian spices. The Indonesian Spices Dataset, containing 6,510 images from Kaggle, was divided into 80% training, 10% validation, and 10% testing sets. Data augmentation techniques, such as random horizontal flipping and rotation, were implemented to enhance model generalization and reduce overfitting. The model was trained for over 20 epochs using the AdamW optimizer with cosine learning rate scheduling. Results indicate that the proposed model achieved a test accuracy of 97%, with macro average precision, recall, and F1-score also at 97%. The minimal difference between training and validation accuracy demonstrates robust generalization to unseen data. The model is computationally efficient and suitable for deployment on edge devices, supporting applications in agribusiness for automated spice identification, quality control, and education]]>
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