Article Per Year (5 Year)
p-Index From 2019 - 2024
0.408
P-Index
This Author published in this journals
All Journal IJISTECH (International Journal Of Information System & Technology) IJISTECH
Lely Sahrani
Department of computer science, Facultay Science and Technology,Universitas Islam Negeri Sumatera Utara, Medan, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences

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

Found 1 Documents
Search
Journal : IJISTECH (International Journal Of Information System

 1 
Classification of Tomato Leaf Based on Gabor Filter Extraction And Support Vector Machine Algorithm Mhd. Furqan; A Armansyah; Lely Sahrani
IJISTECH (International Journal of Information System and Technology) Vol 4, No 2 (2021): May
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (680.977 KB) | DOI: 10.30645/ijistech.v4i2.173

Abstract

Tomato production in Indonesia is reduced because tomato leaves are stricken with disease. The main disease that often attacks tomato leaves is rotten leaves and bacterial patches or commonly called dry patches. Identification of tomato leaf disease is still done manually with human vision. The shortcomings of the method manually required a technology that is able to extract the texture of tomato leaf disease. One of them is by the process of extracting the texture of leaves with gabor filters, namely by using frequency and orientation parameters. Based on the results of the experiment obtained that the input parameter gabor filter with orientation of 90o with a combination of frequency 4 produces a fairly clear contrast. The process of extracting the texture of the leaf aims to get the magnitude value of the tomato leaf that will be used as inputs for the classification process. The svm algorithm grouped data that had the same characteristics into one class. Training data used 42 images and test data used 30 images, with the success rate of 83.33%.
Co-Authors Armansyah Armansyah Mhd Furqan