10 Citations (Scopus)

Abstract

The process of identifying bacteria is an essential factor in the medical field. One of the germs that cause lung damage in pneumonia is gram-negative bacteria. The convolutional neural network method is the newest approach to machine learning because it has a high degree of accuracy. But the drawback is due to his in-depth knowledge, the computation time for the training process takes a long time. The method offered in this research is automatic contrast addition in the preprocessing stage and the use of custom layers. Also, augmentation data added to increase the variation in the amount of data in the training process. In using custom layers, the objective is to obtain minimal computational training time while maintaining maximum accuracy values. The results show that an average accuracy around 98.59% with average training time around 01 minutes 56 seconds, average MSE 0.0274, RMSE 0.1693, and MAE 0.0185.

Original languageEnglish
Title of host publicationCENIM 2020 - Proceeding
Subtitle of host publicationInternational Conference on Computer Engineering, Network, and Intelligent Multimedia 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages70-75
Number of pages6
ISBN (Electronic)9781728182834
DOIs
Publication statusPublished - 17 Nov 2020
Event2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020 - Virtual, Surabaya, Indonesia
Duration: 17 Nov 202018 Nov 2020

Publication series

NameCENIM 2020 - Proceeding: International Conference on Computer Engineering, Network, and Intelligent Multimedia 2020

Conference

Conference2020 International Conference on Computer Engineering, Network, and Intelligent Multimedia, CENIM 2020
Country/TerritoryIndonesia
CityVirtual, Surabaya
Period17/11/2018/11/20

Keywords

  • Gram-negative bacteria
  • Pneumonia
  • auto contrast Convolutional neural network
  • data augmentation

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