<![CDATA[Difficulties in manually tracking nutrition lead to the need for automatic food detection systems. However, Indonesian food presents tough challenges to recognize because similar-looking foods and different serving styles make it hard. This study looks at two deep learning models that follow different approaches: YOLOv8, which is known for being fast and efficient, and Faster R-CNN, which is known for being very accurate. The goal is to find the best model for use on mobile devices. This research uses a strict and standardized way to test the models to make sure the comparison is fair. A public dataset with 1,325 images from Roboflow was used. To deal with uneven class distribution, the images were split using Stratified Random Sampling. Before training, the images were resized using letterbox method to keep their original shape and normalized for pixel values. Both models were trained for the same number of epochs (100) and used the same optimizer (SGD) to ensure fair comparisons. The results show that YOLOv8 performs better in all areas. It achieved 88.6% mAP@50 accuracy and 62.0% mAP@50-95 precision. Faster R-CNN got 85.5% and 55.6% respectively. YOLOv8 also excels in sensitivity or Recall, reaching 87.7% compared to 61.7% for Faster R-CNN. The F1-Score, which balances accuracy and sensitivity, is 84.0% for YOLOv8 and 72% for Faster R-CNN. In terms of speed and size, YOLOv8 is much better. It runs in 13.5 ms and is 21.5 MB in size. That makes it 7.7 times faster and 7.3 times smaller than Faster R-CNN. Based on these results, YOLOv8 is the best choice for developing mobile-based nutrition tracking systems.]]>
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