Syahputra, Wahyu Firman (2022) TRANSFER LEARNING CONVOLUTIONAL NEURAL NETWORK PADA KLASIFIKASI PENYAKIT SINGKONG BERDASARKAN CITRA DAUN. Undergraduate thesis, UPN Vetran Jawa Timur.
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Abstract
In recent years, there have been many developments in machine learning and digital image processing, which show great potential in helping accelerate the diagnosis of diseases in plants, especially cassava. In this system, the algorithm used to carry out the classification is the Transfer Learning Convolutional Neural Network. The data used is obtained from Kaggle entitled 'Cassava Disease Classification' which contains images of cassava leaves. The dataset has data of 5,696 color images which have 5 classes with a distribution of 4 classes of diseased cassava plant leaves (Cassava Bacterial Blight, Cassava Brown Streak Disease, Cassava Green Mite, Cassava Mosaic Disease) and 1 class of Healthy cassava plant leaves. The system has been successfully implemented in this research. The test results of the machine learning model using the Transfer Learning Convolutional Neural Network algorithm get the best accuracy in Test Scheme II. The learning model in this scheme can achieve an accuracy of 80%. To be able to take advantage of the model that has been trained so that it can be easily used, the model is embedded in the Android application.
| Item Type: | Thesis (Undergraduate) | ||||||||||||
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| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science | ||||||||||||
| Divisions: | Faculty of Computer Science > Departemen of Informatics | ||||||||||||
| Depositing User: | WAHYU FIRMAN SYAHPUTRA | ||||||||||||
| Date Deposited: | 23 Nov 2022 06:49 | ||||||||||||
| Last Modified: | 23 Nov 2022 06:49 | ||||||||||||
| URI: | http://repository.upnjatim.ac.id/id/eprint/10393 |
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