Authors:
Rizal Amegia Saputra
1
;
Diah Puspitasari
2
;
Adi Supriyatna
2
;
Dede Saefudin
1
;
Rachmat Purnama
2
and
Kresna Ramanda
2
Affiliations:
1
Universitas Bina Sarana, Sukabumi, Indonesia
;
2
Universitas Bina Sarana Informatika, Jakarta, Indonesia
Keyword(s):
Hyperparameter Optimization, CNN Algorithm, Chili Leaf Disease.
Abstract:
Diseases of a plant will greatly affect the yield. Chilli plants are one of the most frequently used food ingredients in various dishes in Indonesia. Leaves on chili plants are often affected by the disease, if the condition is not treated immediately, the disease can damage plants and result in crop failure, early detection of chili plant diseases is very important to do, to reduce the risk of crop failure. Technological developments and the application of deep learning algorithms can monitor chili plants automatically using a computer system. Using this algorithm, the system will analyze and identify diseases that can be seen and recorded by the camera. In this study, the proposed method uses the CNN algorithm by optimizing hyperparameters. The optimizers used are Adam, Nadam, SGD, RMSProp, and Adadelta with Epoch 50 and 100, Learning Rate 0.1, and Batch Size 8, 16, and 32. From the Optimizer used, the Nadam optimizer at epoch 100, batch size 16, learning rate 0.1 gives the most op
timal results with 86% accuracy, 86% precision, 84% recall, and 84% f1-score. It is proven that the CNN algorithm and the Nadam architecture are well capable of classifying data according to its class.
(More)