IMPLEMENTASI PERANGKAT IOT KAMERA CCTV BERBASIS YOLO DAN KALMAN FILTER UNTUK MANAJEMEN KETERSEDIAAN SLOT DAN JUMLAH KENDARAAN : IMPLEMENTATION OF AN IOT-BASED CCTV CAMERA SYSTEM USING YOLO AND KALMAN FILTER FOR PARKING SLOT AVAILABILITY AND VEHICLE COUNT MANAGEMENT IN HOTEL PARKING AREAS - Muhammad Iqbal Falevy; Aryanti Aryanti; Suzan Zefi (Rabit : Jurnal Teknologi dan Sistem Informasi Univrab, 2026)
Real-time parking availability information is an important factor in improving service quality in the hospitality industry. Conventional parking management still has limitations in providing accurate and timely information regarding vehicle occupancy and parking slot availability. This study aims to implement an Internet of Things (IoT) and Computer Vision-based hotel parking monitoring system using ESP32-CAM, the YOLOv8-L deep learning model, Kalman Filter through the SORT algorithm, Firebase Realtime Database, and a web-based monitoring dashboard. ESP32-CAM transmits video streams to the server, where YOLOv8-L performs real-time vehicle detection, while Kalman Filter is used for vehicle tracking and counting. The processing results are synchronized with Firebase Realtime Database and displayed on the monitoring dashboard. Experimental results show that the system successfully detected 14 vehicles out of a total parking capacity of 20 slots, resulting in 6 available parking spaces. The system was also able to perform real-time vehicle tracking and counting while automatically updating parking information on the dashboard. The proposed system can support hotel parking management effectively, efficiently, and in real time.