Klasifikasi Kualitas Biji Kedelai Menggunakan Transfer Learning Convolutional Neural Network Dan SMOTE

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Soybeans are an important commodity in the food and feed industry, where they can be used to produce soy milk or other processed products. However, low-quality seeds can reduce the quality of the processed products and increase production costs. To address this issue, a soybean seed quality classification system was developed using the method of Transfer Learning with CNN and SMOTE. This method leverages the ability of neural networks for extracting of the visual features and handle for data imbalance between classes. The study shows that the CNN model achieved value accuracy is 91.09%, while the combination of CNN and SMOTE get the accuracyf 89.92%. Additionally, the MobileNetV2 model reached an accuracy of 91.11%, which further improved to 92.42% after applying SMOTE. These results demonstrate that the use of Transfer Learning and SMOTE significantly enhances accuracy in soybean seed quality classification, resulting in a more effective.