PERANCANGAN PURWARUPA ALAT DETEKSI KERUSAKAN JALAN BERBASIS MACHINE LEARNING - Okta Arianta, I Wayan; Elluya Sitompul, Steven Tulus Parulian; Dharmawan, Gede Candra Maha; Indra ER, Ngurah; Jasa, Lie; Setiawan, Widyadi; Elba Duta Nugraha, I Putu (Jurnal SPEKTRUM; Vol 12 No 2 (2025): Jurnal SPEKTRUM, 2025)
The lengthy process of road damage detection is one of the cause of delays of road repair processes in Indonesia. Manual detection method takes a lot of time and is highly prone to errors. Although several technologies have been developed to solve this problem, the high cost and complexity of use often become significant barriers. This research aims to design a prototype of a low-cost road damage detection tool that can help accelerate the process of road damage detection and inspection in Indonesia. The system uses remote sensing principles with YOLOv8s Object Detection technology to detect four classes of road damage. The prototype was designed using alternative electronic components powered by Raspberry Pi 5 and Hilo-8L accelerator. and Hailo-8L accelerator. The use of this alternative device is capable of running Object Detection model as well as other computational tasks stably with an average of ?27 FPS. The Ublox GPS Module Neo-7M GPS Module used in the system has a deviation of ±3.5 meters. The designed object detection model has an overall accuracy of 92,96%. Testing results of the prototype in stationary conditions showed an optimal angle of 20-30 degrees with detection distances of 1, 3, and 5 meters. In moving conditions, optimal system performance is at a speed of 20-30 km/h with effective light intensity in the morning and afternoon.