Implementation of YOLOv11 for Food Detection to Support Nutritional Information in Stunting Prevention
DOI:
10.33395/sinkron.v10i1.15553Keywords:
Deep learning, food detection, nutrition education, stunting prevention, YOLOv11Abstract
Stunting remains a persistent public health challenge in Indonesia, mainly due to chronic malnutrition and limited parental literacy regarding balanced diets. To address this issue, this study developed an integrated nutrition education system using YOLOv11 and Generative AI, structured based on the ADDIE framework. This system aims to bridge the literacy gap by automating food identification and transforming technical nutritional data into easy-to-understand insights for stunting prevention. The study used a dataset of 2,413 images, which was expanded to 4,687 through augmentation. Technical evaluation showed strong performance with a Mean Average Precision (mAP@0.5) of 97%, ensuring reliable detection of important protein sources such as eggs. In addition to accuracy, the system applies a heuristic nutritional assessment algorithm visualized through a ‘Traffic Light’ system to reduce the cognitive load on users. Qualitative evaluation with posyandu cadres showed a significant increase in nutritional understanding, with 90% of users able to explain appropriate dietary interventions based on AI recommendations. These results conclude that the integration of computer vision with structured educational design effectively transforms mobile devices into real-time decision support systems for stunting prevention initiatives at the community level.
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Copyright (c) 2025 Dian Restu Adji, Erba Lutfina, Resha Meiranadi Caturkusuma, Galuh Wilujeng Saraswati, Wildan Mahmud
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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