<![CDATA[Animal classification is a complex challenge due to variations in shape, color, and patterns across species. Traditional methods, which rely on manual feature extraction, are often ineffective in handling such complexities. Therefore, this study employs Convolutional Neural Networks (CNNs) as a more accurate approach for automatic feature extraction and image classification. This research aims to develop an animal image classification model, specifically for dogs, cats, and tigers, utilizing CNNs. The dataset consists of 4,800 images obtained from Kaggle, which were divided into training, testing, and validation sets. The CNN model was built using TensorFlow/Keras, trained for 50 epochs, and evaluated using accuracy, precision, recall, F1-score, and a confusion matrix. The experimental results show that the model achieved an overall accuracy of 88%, with the highest performance in tiger classification (99% accuracy). However, distinguishing between dogs and cats remains a challenge, with an accuracy of 81% for both classes. The findings indicate that CNNs are effective in automatically classifying animal images, although challenges persist in differentiating visually similar species. This study lays the groundwork for further enhancements, such as refining the model architecture or utilizing data augmentation techniques to boost classification accuracy.]]>
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