Herbal Leaf Image Classification Using Convolutional Neural Network (CNN)

Mujahid, Putra Edi
Manik, Rosianni
Simbolon, Junpri Sardodo
Sinaga, Maria Riska Ratna Sari
Aisyah, Siti
Nababan, Marlince
Harmaja, Okta Jaya

Jusikom: Jurnal Sistem Informasi Ilmu Komputer
Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER

This research delves into the application of Convolutional Neural Networks (CNNs) to address the complexities of identifying herbal leaf species in Indonesia, often challenging due to the vast variations in shape, color, and texture. Utilizing a dataset of herbal leaf images acquired using the Bing Downloader Scrapping technique, a CNN model was trained to classify various plant varieties with a remarkable accuracy rate of 92.66%. Additionally, the analysis of low loss values indicates that the model not only effectively maps the intricate features of each image to the correct category but also efficiently reduces error rates. These findings offer a significant contribution to the context of herbal medicine development and biodiversity conservation, opening up avenues for technological integration in efforts to preserve Indonesia's natural and cultural resources.