Herbal Leaf Image Classification Using Convolutional Neural Network (CNN)

Authors

  • Putra Edi Mujahid Universitas Prima Indonesia
  • Rosianni Manik a:1:{s:5:"en_US";s:27:"Universitas Prima Indonesia";}
  • Junpri Sardodo Simbolon Universitas Prima Indonesia
  • Maria Riska Ratna Sari Sinaga Universitas Prima Indonesia
  • Siti Aisyah Universitas Prima Indonesia
  • Marlince Nababan Universitas Prima Indonesia
  • Okta Jaya Harmaja Universitas Prima Indonesia

DOI:

https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5145

Abstract

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.

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Published

2024-08-21

How to Cite

[1]
P. E. Mujahid, “Herbal Leaf Image Classification Using Convolutional Neural Network (CNN)”, JUSIKOM PRIMA, vol. 8, no. 1, pp. 52-68, Aug. 2024.