Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Information Engineering and Science
Arfian Hendro Priyono
Unknown Affiliation
Engineering

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

Found 1 Documents
Search

 1 
Detection of Sugarcane Plant Diseases Based on Leaf Image Using Convolutional Neural Network Method Arfian Hendro Priyono; Ema Utami; Dhani Ariatmanto
International Journal of Information Engineering and Science Vol. 2 No. 2 (2025): May : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i2.252

Abstract

As the primary raw material for sugar and ethanol production, sugarcane is a highly significant plantation commodity. However, its relatively long growing period of approximately one year makes it more susceptible to diseases. Machine learning technology has been applied in the identification of sugarcane leaves, including through pre-processing methods and the development of disease classification models using Convolutional Neural Network (CNN) and Support Vector Machine (SVM) approaches. However, these methods exhibit limitations in terms of accuracy. Therefore, improving identification accuracy using VGG-16 is essential. The objective of this study is to enhance the accuracy of sugarcane leaf disease identification by utilizing VGG-16. The dataset consists of  2,521 sugarcane leaf images categorized into five classes. The results of this study indicate an accuracy improvement from 97.78% to 99.14%, reflecting an increase of 1.36%
Co-Authors DHANI ARIATMANTO Ema Utami