Betawi traditional food image detection using ResNet and DenseNet

Noer Fitria Putra Setyono, Dina Chahyati, Mohamad Ivan Fanany

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Technological developments in the field of Smart System is now growing and began to spread to various areas such as tourism sector. In this research, we developed a smart system for Betawi culinary tourism. Detection of traditional food names using images is a challenge because the variety of shape and direction of shooting is always different. The use of deep learning architecture is expected to overcome the problem, but the selection of effective deep learning architecture is also a problem. This study compares some deep learning architecture to determine the suitable architecture to detect culinary images. Based on our experimental results, DenseNet169 gives the best performance in terms of accuracy, error rate and training time when using CPU and ResNet50 when using GPU..

Original languageEnglish
Title of host publication2018 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages441-445
Number of pages5
ISBN (Electronic)9781728101354
DOIs
Publication statusPublished - 17 Jan 2019
Event10th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2018 - Yogyakarta, Indonesia
Duration: 27 Oct 201828 Oct 2018

Publication series

Name2018 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2018

Conference

Conference10th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2018
CountryIndonesia
CityYogyakarta
Period27/10/1828/10/18

Keywords

  • Betawi
  • Deep learning
  • Image detection
  • Tourism
  • Traditional food

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