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- W4317642563 abstract "In the era of globalization, the popularity of traditional food has been replaced by imported food. In Indonesia, traditional food is starting to lose its meaning and popularity among various generations, including the millennial generation. There are many kinds of traditional food in Indonesia, but some people may not acknowledge the name of traditional food. In this study, an automatic recognized system was developed to classify the traditional food in Indonesia. A Convolutional Neural Network is used to provide a reliable classification of recognizing ten types of Indonesian traditional food: Asinan (Pickled Vegetable), Gado-gado (Mixed vegetables with peanut sauce), Gudeg, Kerak Telor (Egg crust), Kolak (compote), Papeda (sago porridge), Rawon (Indonesian beef soup), Sate Bandeng (Milkfish satay), Sop Buntut (Oxtail soup) and Soto (clear soup). The data set used is 20 images for each food, in total 200 images and divided into three parts training, testing, & validation as much as 70% (140 images), 15% (30 images), 15% (30 images) respectively. By using the EfficientNet architecture, the proposed system can reach an accuracy of 90%. And the implementation of this research is applied to mobile devices using TensorFlow Lite." @default.
- W4317642563 created "2023-01-21" @default.
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- W4317642563 date "2022-11-16" @default.
- W4317642563 modified "2023-09-27" @default.
- W4317642563 title "Indonesian Traditional Food Image Recognition using Convolutional Neural Network" @default.
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- W4317642563 doi "https://doi.org/10.1109/icimcis56303.2022.10017684" @default.
- W4317642563 hasPublicationYear "2022" @default.
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