<![CDATA[Convolutional Neural Networks (CNN) have become the dominant algorithm in image classification and prediction due to their ability to recognize complex visual patterns. However, to achieve optimal performance, CNN models require various optimization techniques. This study aims to explore and implement optimization techniques in training CNN models to enhance the accuracy and generalization capabilities of the model in image classification. The techniques implemented include learning rate scheduling, batch normalization, regularization with dropout and L2, data augmentation, and the use of transfer learning with pre-trained models such as VGG16. Additionally, early stopping methods and advanced optimization algorithms like Adam and RMSprop are applied to improve convergence and prevent overfitting. The results show that the combination of applied optimization techniques significantly improves the performance of CNN models. Analysis of the model training history visualization indicates a reduction in loss and an increase in accuracy, with slight indications of overfitting towards the end of training. These findings emphasize the importance of employing holistic optimization strategies in developing CNN models for image classification and prediction applications. However, in some experiments, the trained models still exhibited prediction errors. This can be attributed to factors such as overfitting or underfitting, data quality and quantity, data diversity, model architecture, and optimization methods. Therefore, further optimization is needed in data preparation, determination of optimization methods, and data cleaning.]]>
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