Pengenalan Makanan Khas Palembang Secara Realtime Menggunakan Yolov8 dan Text to Speech - Valentino, Calvin Bertnas; Hermanto, Dedy (Algoritme Jurnal Mahasiswa Teknik Informatika, 2025)
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.