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All Journal G-Tech : Jurnal Teknologi Terapan
Abdul Rosid
Universitas Ibrahimy, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Energy Engineering

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Klasifikasi Penyakit Tanaman Kentang Berdasarkan Citra Daun dan Batang dengan Metode Convolutional Neural Network dan Gray Level Co-Occurrence Matrix Abdul Rosid; Abd. Ghofur; Firman Santoso
G-Tech: Jurnal Teknologi Terapan Vol 8 No 3 (2024): G-Tech, Vol. 8 No. 3 Juli 2024
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33379/gtech.v8i3.4298

Abstract

The factor that causes potato plants to be less than optimal is diseased potato plants. This potato plant disease can be identified from spotty leaves and dry stems, by identifying it using an identification system based on disease images. Potato stem datasets were obtained at the Ijen Bondowoso plantation as many as 1,132 and 816 diseased and non-diseased potato stem datasets. In the results of the potato leaf graph, the best results were obtained at epoch 25 with an accuracy value of testing data and training data of 82% and 81% with the loss model at epoch 25 being at a value of 0.42 for training data and 0.41 for testing data in the classification of diseased leaves. potato plant. The results of the classification of potato plant stems found the best value at epoch 25 with an accuracy value of 85% on testing data and 86% on training data. The model loss value in the training set is 0.34 and the validation test value is 0.33 at epoch 24
Co-Authors Abd. Ghofur Firman Santoso