Analisis Pengaruh Augmentasi Data pada Model MobileNetV3-Large untuk Klasifikasi Penyakit Daun Labu Manis
DOI:
https://doi.org/10.24252/shift.v6i2.288Keywords:
Classification, Data Augmentation, Deep Learning, mobilenetv3-large, Sweet Pumpkin Leaf DiseaseAbstract
Sweet pumpkin (Cucurbita moschata) is a horticultural commodity with high economic value, but its productivity has frequently decreased due to various leaf diseases. Manual disease identification still has several limitations because it is subjective, time-consuming, and inefficient for large-scale agricultural areas. This study aimed to analyze the effect of data augmentation on sweet pumpkin leaf disease classification performance using the MobileNetV3-Large model supported by Early Stopping and Learning Rate Scheduler optimization techniques. The dataset consisted of 1,000 images representing five classes, namely Healthy Leaf, Downy Mildew, Leaf Curl, Mosaic Disease, and Red Beetle. The dataset was divided into training, validation, and testing subsets using an 80:10:10 ratio. Two experimental scenarios were conducted, namely using the original dataset and the augmented dataset. The experimental results showed that the first scenario achieved an accuracy of 85.00%, while the second scenario improved the accuracy to 88.00%. The precision, recall, and F1-score values also increased from 86%, 85%, and 85% to 87%, 87%, and 87%, respectively. In addition, the model size remained lightweight at 15 MB in both scenarios. The results indicated that the combination of the MobileNetV3-Large model, data data augmentation, Early Stopping, and Learning Rate Scheduler successfully improved model generalization while maintaining computational efficiency.
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