Herbal Leaves Classification Based on Leaf Image Using CNN Architecture Model VGG16

Herbal leaves are a type that is often used by people in the health sector. The problem faced lack of knowledge about types herbal and difficulty distinguishing for ordinary who do not understand plants. If any plant used, it will have negative impact on health. Automatic classification with help technology reduce risk misidentification leaf types. To make identification, precise accurate detection process needed. This research aims to facilitate model images higher accuracy value than previous research. Therefore, proposed method this one Transfer Learning methods, namely Convolutional Neural Network (CNN) pretrained VGG16 model. uses dataset total 10 classes: Belimbing Wuluh, Jambu Biji, Jeruk Nipis, Kemangi, Lidah Buaya, Nangka, Pandan, Pepaya, Seledri Sirih. performance results test using Classification Report shows an increase from 82% 97%. also applies Image Data Generator augmentation which improve image leaves, overfitting, accuracy.