Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI
Setiono, Mukti
Unknown Affiliation
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Klasifikasi Penyakit Antraknosa Citra Cabai Rawit Dengan Metode Convolutional Neural Network (CNN) Setiono, Mukti
JATISI Vol 11 No 2 (2024): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v11i2.8039

Abstract

Abstract Indonesia, as an agricultural country, has a very vital agricultural sector, including the cultivation of cayenne pepper. Cayenne peppers are often infected with anthracnose disease caused by the fungus Colletotrichum sp., causing significant economic losses. This research aims to develop a Convolutional Neural Network (CNN) model for classifying anthracnose in images of cayenne pepper, in order to increase the effectiveness of disease diagnosis. Image data was obtained from chili gardens in Savanajaya Village, Buru Regency, with a total of 1000 images, which were divided into 500 images of healthy chilies and 500 images of infected chilies. The data is processed and labeled manually, then resized for consistency. CNN was trained using the Adam optimizer, RMSprop, and SGDM, with test results showing that the Adam optimizer provided the highest accuracy of 93.25%. The implementation of CNN has proven effective in classifying anthracnose, helping farmers in making timely decisions for disease control, thereby increasing productivity and reducing economic losses. This research emphasizes the importance of choosing the right optimizer and dataset quality in developing image-based plant disease classification models.
Co-Authors