Open AccessIdentifikasi Penyakit Tanaman Ubi Kayu Berdasarkan Citra Daun Menggunakan Metode Probabilistic Neural Network (PNN)
Author(s) -
Yuslena Sari,
Muhammad Alkaff,
Mijani Rahman
Publication year - 2021
Publication title -
jurnal komtika (komputasi dan informatika)
Language(s) - English
Resource type - Journals
eISSN - 2580-734X
pISSN - 2580-2852
DOI - 10.31603/komtika.v5i1.4605
Subject(s) - probabilistic neural network , manihot esculenta , artificial neural network , pattern recognition (psychology) , mathematics , horticulture , artificial intelligence , computer science , biology , time delay neural network
Cassava or better known as cassava is one of the staples of rice which is popular in Indonesia. Cassava plants can flourish in almost all regions of Indonesia. However, cassava is a plant that is susceptible to plant disease, which attacks the disease resulting in a decrease in the amount of productivity of tubers produced by cassava plants. The application of identifying cassava disease based on leaf image is expected to be useful as a support for cassava farming in easily detecting cassava disease, so that it can be dealt with more quickly. This study uses the Gray Level Co-occurrence Matrix (GLCM) method as an extraction feature and the Probabilistic Neural Network (PNN) method for identification processes. Based on the results of tests on 6 types of cassava leaf images, obtained an accuracy of 83.33%.