Lalincek: Sistem Deteksi Otomatis Kendaraan dan Kecelakaan Berbasis CCTV untuk Pemantauan Smart Governance Berbasis Computer Vision - Desnia Anindy Irni Hareva; Mazka Buana Hidayat; Aditya Wisnu Yudha Marsudi (Jurnal Bengawan Solo Pusat Kajian Penelitian dan Pengembangan Daerah Kota Surakarta, 2025)
The City of Surakarta faces serious challenges in traffic safety, with 1,073 accident cases and 41 fatalities in 2024, exacerbated by an average 15-minute delay in accident detection, which can reduce victims’ survival chances by up to 30%. LALINCEK emerges as an innovative solution that utilizes the existing CCTV infrastructure at 84 strategic points across Surakarta to create an automated traffic monitoring and real-time accident detection system. This system integrates YOLOv12 technology with 84.7% vehicle detection accuracy, a homography method for speed estimation with 92% precision, and a deep learning algorithm for anomaly detection with a response time of less than 30 seconds. Unlike conventional systems that rely on manual reports, LALINCEK proactively identifies three critical anomaly types: extreme braking, sudden direction changes, and inter-vehicle distance analysis, then automatically sends notifications to the Transportation Department (Dishub), Police, and Hospitals for a coordinated response. The implementation of this system has the potential to reduce accident response time by up to 60%, increase human resource efficiency by 70%, and directly support Surakarta Zero Accident. LALINCEK can be seamlessly integrated with the Surakarta Smart Governance ecosystem through the platform https://ccroom-dishub.surakarta.go.id/, contributing to four dimensions of Smart City: Smart Governance (service digitalization), Smart Living (enhancing safety), Smart Environment (emission monitoring), and Smart Society (community empowerment), making it a technological investment that delivers real impact on the safety of Surakarta’s citizens.