IMPLEMENTASI MODEL DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK (CNN) PADA CITRA PENYAKIT DAUN JAGUNG UNTUK KLASIFIKASI PENYAKIT TANAMAN

Andhika Bagas Prakosa (a:1:{s:5:"en_US"s:32:"Universitas Kristen Satya Wacana"})

JUKANTI (Jurnal Pendidikan Teknologi Informasi)
Vol 6 No 1 (2023): Jurnal Pendidikan Teknologi Informasi (JUKANTI) Edisi April 2023

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