Traffic management is essential for preventing traffic congestion and regulating traffic flow. Jakarta has implemented several methods to reduce traffic congestion in response to the growing number of vehicles on the road. This study proposes integrating computer vision and fuzzy logic into traffic lights for use in traffic management. The study focuses on comparing and implementing three computer vision libraries: ImageAI, Cloud Vision, and OpenCV Libraries. This comparison is based on Jakarta traffic data captured by closed-circuit television (CCTV). After evaluating the libraries, the result indicates the ImageAI achieves better performance than the other libraries across testing measurements and it was used to develop a module simulator. This simulator was used to compare the amber time produced by each lane at a simple intersection under fixed time versus proposed-actuated traffic lights. The simulation of the proposed actuated traffic light scenario demonstrates that the use of computer vision and fuzzy logic in the decision-making process can marginally improve traffic simulation compared to the fixed-time traffic light scenario. This study illustrates the application of computer vision and fuzzy logic to traffic management. The application of these technologies to traffic lights has the potential to increase traffic flow and decrease congestion.