Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal JUKANTI (Jurnal Pendidikan Teknologi Informasi) Jurnal Pendidikan Teknologi Informasi (JUKANTI)
Andhika Bagas Prakosa
a:1:{s:5:"en_US";s:32:"Universitas Kristen Satya Wacana";}
Computer Science & IT Decision Sciences, Operations Research & Management Education Languange, Linguistic, Communication & Media Other

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

Found 2 Documents
Search

 1 
IMPLEMENTASI MODEL DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK (CNN) PADA CITRA PENYAKIT DAUN JAGUNG UNTUK KLASIFIKASI PENYAKIT TANAMAN Andhika Bagas Prakosa
Jurnal Pendidikan Teknologi Informasi (JUKANTI) Vol 6 No 1 (2023): Jurnal Pendidikan Teknologi Informasi (JUKANTI) Edisi April 2023
Publisher : Program Studi Pendidikan Informatika, Universitas Citra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37792/jukanti.v6i1.919

Abstract

Corn is one of the main ingredients or staple besides rice. In agriculture’s world, leaf diseases appear that cause hampered and disturbed of corn plant growth. This research has a purpose to give a solution to detect whether the corn plant is diseased or not. Classification of leaf disease corn plants use a deep learning model Convolutional Neural Network (CNN). On this test, a numbered dataset is used: 3718 images for healthy corn leaf, and 3814 images for disease corn leaf in this case common rust. From this test result with data ratio 40% for data test, and 60% data train within training of 50 epochs obtained accuracy get a value of 0.9990, precision value get a value by 0.9981, recall get a value by 1, and F1 score get a value by 0,9990
IMPLEMENTASI MODEL DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK (CNN) PADA CITRA PENYAKIT DAUN JAGUNG UNTUK KLASIFIKASI PENYAKIT TANAMAN Andhika Bagas Prakosa
Jurnal Pendidikan Teknologi Informasi (JUKANTI) Vol 6 No 1 (2023): Jurnal Pendidikan Teknologi Informasi (JUKANTI) Edisi April 2023
Publisher : Program Studi Pendidikan Informatika, Universitas Citra Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37792/jukanti.v6i1.919

Abstract

Corn is one of the main ingredients or staple besides rice. In agriculture’s world, leaf diseases appear that cause hampered and disturbed of corn plant growth. This research has a purpose to give a solution to detect whether the corn plant is diseased or not. Classification of leaf disease corn plants use a deep learning model Convolutional Neural Network (CNN). On this test, a numbered dataset is used: 3718 images for healthy corn leaf, and 3814 images for disease corn leaf in this case common rust. From this test result with data ratio 40% for data test, and 60% data train within training of 50 epochs obtained accuracy get a value of 0.9990, precision value get a value by 0.9981, recall get a value by 1, and F1 score get a value by 0,9990
Co-Authors