<![CDATA[This study investigates the impact of employing the transfer learning method on improving flower recognition performance using Convolutional Neural Network (CNN) models. The dataset used consists of 4242 flower images divided into five classes: daisy, tulip, rose, sunflower, and dandelion. This research implements three models: basic CNN, VGG16, and EfficientNetB3, to test the effectiveness of transfer learning in flower classification. The basic CNN model achieved a training accuracy of 73.38% and a validation accuracy of 71.76%, but it generally fails to generalize to new data. The VGG16 model achieved perfect training accuracy but experienced overfitting, with validation accuracy stabilizing around 85-90%. Meanwhile, the EfficientNetB3 model with transfer learning reached a training accuracy of 98.50% and a validation accuracy of 94.00%, demonstrating strong generalization without significant overfitting. The experiment was conducted using data augmentation techniques, and performance evaluation was carried out using accuracy, precision, and recall metrics. The results show that transfer learning with the EfficientNetB3 model provides the best performance in flower classification compared to the basic CNN and VGG16 models. For future research, further development can be done by expanding the types of flower datasets and applying additional optimization techniques to improve accuracy in more complex models.]]>
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