Sefrila, Eren Dio (2024) IMPLEMENTASI ARSITEKTUR INCEPTIONV3 DENGAN OPTIMASI ADAM, SGD DAN RMSP PADA KLASIFIKASI PENYAKIT MALARIA. Undergraduate thesis, UPN Veteran Jawa Timur.
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Abstract
In the current era of technological advancement, deep learning has become a widely discussed and utilized topic, particularly in image classification, object detection, and natural language processing. A significant development in deep learning is the Convolutional Neural Network (CNN), which is enhanced with various optimizations such as Adam, RMSProp, and SGD. This thesis implements the Inception v3 architecture for the deep learning model, utilizing these three optimization methods to classify malaria disease. The study aims to evaluate performance and determine the best optimization based on classification accuracy. The results indicate that the SGD optimization with a learning rate of 0.001 achieved an accuracy of 94%, RMSProp with learning rates of 0.001 and 0.0001 achieved an accuracy of 96%, and Adam with learning rates of 0.001 and 0.0001 achieved an accuracy of 95%.
| Item Type: | Thesis (Undergraduate) | ||||||||||||
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| Subjects: | T Technology > T Technology (General) > T58.6-58.62 Management Information Systems | ||||||||||||
| Divisions: | Faculty of Computer Science > Departemen of Informatics | ||||||||||||
| Depositing User: | Eren Eren Eren | ||||||||||||
| Date Deposited: | 05 Jun 2024 03:46 | ||||||||||||
| Last Modified: | 05 Jun 2024 03:46 | ||||||||||||
| URI: | https://repository.upnjatim.ac.id/id/eprint/23326 |
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