Fatikahsari, Alya Safira (2023) Perbandingan Algoritma Convolutional Neural Network dan K-Nearest Neighbors Pada Klasifikasi Citra Penyakit Daun Jagung. Undergraduate thesis, UPN Veteran Jawa Timur.
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Abstract
Corn or which has the scientific name zea mays is one of them plants that have a high carbohydrate content besides rice and wheat. On In this study, researchers classified 4,000 corn leaf diseases for 4 classes namely Blight, Common rust, Gray leaf spot, and Healthy. Researcher using the Convolutional Neural Network algorithm ResNet-50 architecture and K-Nearest Neighbor to classify limited datasets. In the initial stage there is a pre-processing stage, followed by design CNN Algorithm ResNet-50 and KNN. Then the dataset will be divided into data train and test data. After carrying out the model training process, it is carried out testing and proceed with evaluating the model. After that compare the results of model evaluation to determine the performance of the algorithm CNN ResNet-50 and KNN. The CNN ResNet-50 algorithm gets the best accuracy results with using 80% training data, 20% test data and a learning rate of 0.001 and get an accuracy weight of 96%. The KNN algorithm gets results lower accuracy of 81% with 75% training data, 25% test data and random state of 40.
| Item Type: | Thesis (Undergraduate) | ||||||||||||
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| Subjects: | T Technology > T Technology (General) | ||||||||||||
| Divisions: | Faculty of Computer Science > Departemen of Informatics | ||||||||||||
| Depositing User: | Alya Safira Informatika | ||||||||||||
| Date Deposited: | 17 May 2023 07:35 | ||||||||||||
| Last Modified: | 17 May 2023 07:35 | ||||||||||||
| URI: | http://repository.upnjatim.ac.id/id/eprint/13106 |
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