DETEKSI CITRA DIGITAL PENYAKIT CACAR MONYET MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK DENGAN ARSITEKTUR MOBILENETV2

Putri Sarah Fransisca, . (2023) DETEKSI CITRA DIGITAL PENYAKIT CACAR MONYET MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK DENGAN ARSITEKTUR MOBILENETV2. Skripsi thesis, Universitas Pembangunan Nasional Veteran Jakarta.

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Abstract

In July 2022, monkeypox was declared a global health emergency due to its occurrence in more than 70 countries. The first case of monkeypox in Indonesia was discovered in Jakarta in August 2022. Differentiating between monkeypox, chickenpox, and measles, which share similar symptoms, poses a challenge for healthcare workers. To address this, a research study was conducted to develop an automated algorithm for detecting digital images of monkeypox. The algorithm used was a Convolutional Neural Network with MobileNetV2 architecture, implementing transfer learning. The model was trained for a total of 5 epochs and utilized two types of optimizers, namely Adam and RMSprop. Applying Adam Optimizer with a learning rate of 10-4 resulted in a test accuracy of 94%, training accuracy of 92%, and a loss function value of 27%. On the other hand, implementing RMSprop Optimizer with a learning rate of 45×10-3 achieved a test accuracy of 97%, training accuracy of 97%, but with a relatively higher loss function value of 52%.

Item Type: Thesis (Skripsi)
Additional Information: [No.Panggil: 1910511037] [Pembimbing: Nurhafifah Matondang] [Penguji 1: Ermatita] [Penguji 2: Yuni Widiastiwi]
Uncontrolled Keywords: Monkeypox, Skin Lesion Disease, MobileNetV2, Convolutional Neural Network, Image Recognition.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Fakultas Ilmu Komputer > Program Studi Informatika (S1)
Depositing User: Putri Sarah Fransisca
Date Deposited: 25 Jul 2023 06:55
Last Modified: 25 Jul 2023 06:55
URI: http://repository.upnvj.ac.id/id/eprint/24552

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