Implementation of deep learning convolutional neural network in identifying food ingredients for complementary feeding recommendation

Handoko, R, Natalia, F, Sudirman, S ORCID: 0000-0003-4083-0810 and Ko, CS (2026) Implementation of deep learning convolutional neural network in identifying food ingredients for complementary feeding recommendation. ICIC Express Letters Part B Applications, 17 (1). pp. 105-112. ISSN 2185-2766

Preview

Text IMPLEMENTATION OF DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK IN IDENTIFYING FOOD INGREDIENTS FOR COMPLEMENTARY FEEDING RECOMMENDATION.pdf - Published Version Download (1MB) | Preview

Abstract

Indonesia is facing a serious issue of malnutrition, resulting in stunting among infants and toddlers. Stunting can impede physical growth, health, and cognitive development in children. The government highlights the importance of education on exclusive breastfeeding and complementary feeding rich in protein as effective preventive measures. Limited understanding of complementary feeding hinders the selection of appropriate menus and cooking techniques to provide adequate nutrition to children. This research is based on the implementation of one of the deep learning algorithms, Convolutional Neural Network (CNN), to recognize and classify images by comparing the performance of two transfer learning models, VGG16 and MobileNetV2. The models are trained on images of high-protein food ingredients suitable for children aged 6 to 23 months, as recommended by the Ministry of Health. The best model is implemented in an Android application to provide Complementary Foods for Breast Milk (CFBM) recommendations. The application is developed using the Android Studio IDE with the Kotlin programming language. The model will be implemented using TensorFlow Lite. This application will provide recommendations for CFBM menu recipes based on submitted food ingredients, and users can save their favorite menus using the favorite feature.

Item Type:	Article
Subjects:	T Technology > TX Home economics > TX341 Nutrition. Foods and food supply Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Divisions:	Computer Science and Mathematics
Publisher:	ICIC International
Date of acceptance:	1 May 2025
Date of first compliant Open Access:	28 September 2026
Date Deposited:	28 Sep 2026 14:59
Last Modified:	28 Sep 2026 14:59
DOI or ID number:	10.24507/icicelb.17.01.105
URI:	https://researchonline.ljmu.ac.uk/id/eprint/29549

View Item