<![CDATA[Stunting has a high prevalence of 21.6% from the government target of 14% and is one of the health problems in Indonesia. Lack of nutrition, especially protein, is the main cause that plays a role in child growth. One of the preventive solutions is to provide protein-rich complementary foods (MP-ASI). To enhance this solution, technology that can swiftly and precisely identify high-protein food components is imperative. This research seeks to create a high-protein food detection model utilizing the YOLOv11 framework, chosen for its efficacy in object detection, particularly in intricate environments and with overlapping items. The research methodology includes several stages: dataset collection and annotation, data pre-processing, model training, model evaluation, and model testing. The dataset is divided into three parts: 70% for the training set, 20% for the validation set, and 10% for the test set. The YOLOv11s model is used for training. Evaluation is based on precision, recall, and mean Average Precision (mAP) metrics to ensure the model’s detection accuracy. The evaluation results indicate a precision of 96%, recall of 92.3%, mAP50 of 96.4%, and mAP50-95 of 81.5%. During testing, the model achieved a success rate of 98.2%. These results demonstrate the model’s potential in detecting protein-rich foods, which could significantly contribute to addressing malnutrition and stunting.]]>
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