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All Journal Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Eren Dio Sefrila
Universitas Pembangunan Nasional “Veteran” Jawa Timur
Computer Science & IT

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Implementasi Arsitektur Inception V3 Dengan Optimasi Adam, SGD dan RMSP Pada Klasifikasi Penyakit Malaria Eren Dio Sefrila; Basuki Rahmat; Andreas Nugroho Sihananto
Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi Vol. 2 No. 2 (2024): Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/bridge.v2i2.62

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%.
Co-Authors Andreas Nugroho Sihananto Basuki Rahmat