<![CDATA[Identifying foot arch types is crucial for maintaining health and comfort. Flat foot arches can cause pain and discomfort, potentially interfering with activities such as sports. This research aims to develop an Artificial intelligence (AI)-based application to detect normal and flat foot arch types through X-ray images. The YOLOv8 model with bounding box is converted to TensorFlow Lite format to be integrated into a mobile platform through Android Studio. The application uses a waterfall model without maintenance, starting from the analysis of x-ray dataset needs, development and testing of the YOLOv8 model, conversion to TensorFlow Lite, design, black box testing, and application on Android devices. This application can only identify x-ray photos of the soles of the feet looking right and left. Confusion matrix application testing with 150 epochs shows performance with recall 86.2%, precision 77.1%, accuracy 83.3%, mAP50 94.9%, and mAP50-95 76.2%. Black box testing on mobile devices using datasets augmented with 45° horizontal shear and 90° rotation resulted in maximum identification accuracy compared to traditional methods such as the wet foot test. Traditional methods print the soles of the feet with an identification process that requires precision of the patient's standing position. This app detects flatfoot early, improving comfort in daily activities and sports.]]>
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