Ensemble Learning Development Based on Transfer Learning for Indonesian Traditional Food Detection

Abstract

Development of traditional food competes with other traditional foods now. They must compete with fast food and food from abroad. In 2013, the food and beverage sector were the second highest contributor to tourist expenditure after accommodation. This shows its very important role in the economy. That caused, we need a model that can predict traditional Indonesian foods and snacks. We used ensemble learning. It had 2 transfer learning methods, namely VGG-19 and Xception. They will be combined to improve the performance of the existing model. The research result shown output. It has found that the ensemble learning model achieved accuracy of up to 97% on training data and 91% on testing data. It is hoped that this prediction model can help people recognize typical Indonesian food and increase interest in and preserve the food around them.