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

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

classification, herbal leaf, transfer learning, VGG16.

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

Bella Dwi Mardiana1
, Wahyu Budi Utomo2
, Ulfah Nur Oktaviana3

Agus Eko Minarno5

01-02-2023

Fajar bagus W

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Indonesia

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