@inproceedings{06e434a1d8cb433fb3212203323fc988,
title = "Betawi traditional food image detection using ResNet and DenseNet",
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..",
keywords = "Betawi, Deep learning, Image detection, Tourism, Traditional food",
author = "Setyono, {Noer Fitria Putra} and Dina Chahyati and Fanany, {Mohamad Ivan}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 10th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2018 ; Conference date: 27-10-2018 Through 28-10-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/ICACSIS.2018.8618175",
language = "English",
series = "2018 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "441--445",
booktitle = "2018 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2018",
address = "United States",
}