TY - GEN
T1 - Leftovers Food Recognition using Deep Neural Network and Regression Approach for Objective Visual Analysis Estimation
AU - Sari, Yuita Arum
AU - Adinugroho, Sigit
AU - Maligan, Jaya Mahar
AU - Candra, Ersya Nadia
AU - Utaminingrum, Fitri
AU - Nur'aini, Nabila
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Understanding the nutritional intake is essential for basic life since every human being must have insight into what food they have eaten. A nutritionist can help in guiding what the body should consume, where each patient may have different diet and treatment patterns. One indicator used by dietitians or nutritionists is by estimating the leftovers consumed by the patient. They measure it by visually named Comstock method, which is divided into scales. This method's drawback is subjective from one another dietitians or nutritionists so that an objective assessment with a machine learning-based approach is acquired. This paper proposes a novel stage of defining food recognition and measuring its leftovers using visual analysis. The food image recognition method used CNN to estimate food waste using pixel-based AFLE and regression approach to fit into six scales. The best result of food image recognition was 92.5% using dropout 0.3 with image augmentation and ReLu activation function, while the accuracy result of visual estimation application compared to experts was 85%. It is proved that the combined proposed algorithm is robust for the application of recognizing and estimating leftovers.
AB - Understanding the nutritional intake is essential for basic life since every human being must have insight into what food they have eaten. A nutritionist can help in guiding what the body should consume, where each patient may have different diet and treatment patterns. One indicator used by dietitians or nutritionists is by estimating the leftovers consumed by the patient. They measure it by visually named Comstock method, which is divided into scales. This method's drawback is subjective from one another dietitians or nutritionists so that an objective assessment with a machine learning-based approach is acquired. This paper proposes a novel stage of defining food recognition and measuring its leftovers using visual analysis. The food image recognition method used CNN to estimate food waste using pixel-based AFLE and regression approach to fit into six scales. The best result of food image recognition was 92.5% using dropout 0.3 with image augmentation and ReLu activation function, while the accuracy result of visual estimation application compared to experts was 85%. It is proved that the combined proposed algorithm is robust for the application of recognizing and estimating leftovers.
KW - Comstock visual analysis
KW - Food recognition
KW - leftovers estimation
UR - https://www.scopus.com/pages/publications/85124263733
U2 - 10.1109/IC2IE53219.2021.9649045
DO - 10.1109/IC2IE53219.2021.9649045
M3 - Conference contribution
AN - SCOPUS:85124263733
T3 - Proceedings - 2021 4th International Conference on Computer and Informatics Engineering: IT-Based Digital Industrial Innovation for the Welfare of Society, IC2IE 2021
SP - 24
EP - 29
BT - Proceedings - 2021 4th International Conference on Computer and Informatics Engineering
A2 - Ismail, Iklima Ermis
A2 - Hermawan, Indra
A2 - Rasyidin, Muhammad Yusuf Bagus
A2 - Huzaifa, Malisa
A2 - Muharram, Asep Taufik
A2 - Marcheeta, Noorlela
A2 - Kurniawati, Dewi
A2 - Yuly, Ade Rahma
A2 - Agustin, Maria
A2 - Nalawati, Rizki Elisa
A2 - Nugrahadi, Dodon Turianto
A2 - Budiman, Irwan
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Computer and Informatics Engineering, IC2IE 2021
Y2 - 14 September 2021
ER -