<![CDATA[Abstract−The development of Deep Learning has made significant contributions to the field of image classification, particularly through the use of Convolutional Neural Networks (CNN). However, one of the main problems in implementing CNN models is the suboptimal performance of the model due to inappropriate hyperparameter selection. Simple models such as LeNet are often considered to have limited performance compared to modern architectures, even though with the right approach, this model still has the potential to produce competitive performance. Therefore, this study aims to improve the performance of the LeNet model in image classification through a hyperparameter tuning approach. The methods used in this study include data preprocessing, dataset division, implementation of the LeNet model as a baseline, and hyperparameter optimization including learning rate, batch size, optimizer, and number of epochs. The dataset used is a handwritten number image that has been normalized and transformed to match the CNN model input. Model performance evaluation was carried out using accuracy, precision, recall, and F1-score metrics. The results showed that hyperparameter optimization provided a significant performance improvement over the LeNet model. The baseline model produced an accuracy of 97.52%, while the best optimized model achieved an accuracy of 98.93%. Furthermore, precision, recall, and F1-score values also improved, indicating the model's improved and more balanced classification capabilities. Thus, this study demonstrates that hyperparameter optimization is an effective approach to improving the performance of simple CNN models without increasing architectural complexity.]]>
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