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All Journal SITEKIN: Jurnal Sains, Teknologi dan Industri
Sri Hadianti
University of Nusa Mandiri Jakarta
Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Other

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Bacteria Classification using Image Processing and Residual Neural Network (ResNet) Dybio Dompu Hot Asih; Adnan Purwanto; Dwiza Riana; Sri Hadianti
SITEKIN: Jurnal Sains, Teknologi dan Industri Vol 20, No 1 (2022): Desember 2022
Publisher : Fakultas Sains dan Teknologi Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/sitekin.v20i1.16788

Abstract

Detection of microorganisms is of particular importance to human health and life, and for the industry in general. For this reason, we want this process to be as fast and precise as possible. We also expect that the automation of this activity (detection of microorganisms) can be widely used in various industries. This article is another attempt to the classification of bacteria  that uses a deep learning approach with Residual Neural Network(ResNet) models. The research was conducted by training the ResNet-18,ResNet-34, ResNet50 and ResNet-101 models. The results show that the ResNet-50 and ResNet-101 are the best learning model. It is better to use ResNet-50 than ResNet-101 because of the faster training time. While the results of the research also show that the architecture with the least number of layers is the fastest learning model.  ResNet-50 has an accuracy rate of 96.1% with a training time of 451 seconds is the best learning model. ResNet-18 has an accuracy rate of 93.6% with a training time of 185 seconds is the fastest learning model.
Co-Authors Adnan Purwanto Dwiza Riana Dybio Dompu Hot Asih