<![CDATA[The introduction of traditional Palembang food has an important role in preserving local cultural and culinary heritage. As interest in object recognition technology grows, challenges arise in creating a system that is able to recognize typical types of Palembang food effectively and efficiently. This research aims to overcome these challenges by developing a food detection system based on the You Only Look Once (YOLO) algorithm, which is known for its ability to detect objects in real-time with high accuracy. The dataset used consists of 1,234 images, which are divided into three parts: 70% for training data, 20% for validation data, and 10% for test data. By utilizing YOLO, this system can detect and recognize typical Palembang food in an average time of 3.15 seconds, and achieve an accuracy of 99.28%. Apart from that, this research also integrates a Text-to-Speech feature which provides a verbal description of the detected food, thereby increasing interaction and convenience for users.]]>
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