Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Information Technology and Computer Science Knowledge Engineering and Data Science
Fajri, Diny Melsye Nurul
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Detection of Disease and Pest of Kenaf Plant using Convolutional Neural Network Fajri, Diny Melsye Nurul; Mahmudy, Wayan Firdaus; Yulianti, Titiek
Journal of Information Technology and Computer Science Vol. 6 No. 1: April 2021
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1026.462 KB) | DOI: 10.25126/jitecs.202161195

Abstract

Kenaf fiber is mainly used for forest wood substitute industrial products. Thus, the kenaf fiber can be promoted as the main composition of environmentally friendly goods. Unfortunately, there are several Kenaf gardens that have been stricken with the disease-causing a lack of yield. By utilizing advances in technology, it was felt to be able to help kenaf farmers quickly and accurately detect which pests or diseases attacked their crops. This paper will discuss the application of the machine learning method which is a Convolutional Neural Network (CNN) that can provide results for inputting leaf images into the results of temporary diagnoses. The data used are 838 image data for 4 classes. The average results prove that with CNN an accuracy value of 73% can be achieved for the detection of diseases and plant pests in Kenaf plants.
Detection of Disease and Pest of Kenaf Plant Based on Image Recognition with VGGNet19 Fajri, Diny Melsye Nurul; Mahmudy, Wayan Firdaus; Yulianti, Titiek
Knowledge Engineering and Data Science
Publisher : citeus

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

One of the advantages of Kenaf fiber as an environmental management product that is currently in the center of attention is the use of Kenaf fiber for luxury car interiors with environmentally friendly plastic materials. The opportunity to export Kenaf fiber raw material will provide significant benefits, especially in the agricultural sector in Indonesia. However, there are problems in several areas of Kenaf's garden, namely plants that are attacked by diseases and pests, which cause reduced yields and even death. This problem is caused by the lack of expertise and working hours of extension workers as well as farmers' knowledge about Kenaf plants which have a terrible effect on Kenaf plants. The development of information technology can be overcome by imparting knowledge into machines known as artificial intelligence. In this study, the Convolutional Neural Network method was applied, which aims to identify symptoms and provide information about disease symptoms in Kenaf plants based on images so that early control of plant diseases can be carried out. Data processing trained directly from kenaf plantations obtained an accuracy of 57.56% for the first two classes of introduction to the VGGNet19 architecture and 25.37% for the four classes of the second introduction to the VGGNet19 architecture. The 5×5 block matrix input feature has been added in training to get maximum results.
Co-Authors Titiek YULIANTI Wayan Firdaus Mahmudy