Smart Traffic Light Berbasis Outseal Plc Menggunakan Webcam Dan Ky-037

Abstract

Lampu lalu lintas adalah sinyal yang mengontrol dan mengatur arus lalu lintas. Lampu lalu lintas biasanya dipasang di persimpangan jalan (lintasan penyeberangan). Kemacetan disebabkan oleh beberapa faktor antara lain lampu lalu lintas tidak berfungsi secara efektif. Sistem smart traffic light mampu mendahulukan persimpangan berdasarkan tingkat kepadatan lalu lintas yang berhenti di persimpangan, dimana ketentuan nyala lampu tersebut diatur menggunakan Outseal PLC. Dan juga dapat mengatur traffic light untuk dapat memprioritaskan kendaraan khusus menggunakan sensor KY-037. Dalam penelitian ini melakukan beberapa tahapan antara lain : mengenali masalah, perancangan, pembuatan dan pengujian alat. Dari hasil pengujian yang dilakukan sistem smart traffic light mampu mendahulukan simpang yang mengalami pemadatan kendaraan (kendaraan miniatur) dan sensor KY-037 mampu mendeteksi suara sirine sehingga bisa memprioritaskan kendaraan yang menggunakan sirine serta didapatkan tingkat akurasi alat pada setiap percobaan sebesar 100% dan tingkat presentase error 0%. Traffic lights are signals that control and regulate traffic flow. Traffic lights are usually installed at crossroads (crosswalks). Congestion is caused by several factors, including traffic lights not functioning effectively. The smart traffic light system can prioritize intersections based on the density level of traffic stopping at the intersection, where the conditions for the lights to turn on are regulated using Outseal PLC. And also can set the traffic light to be able to prioritize special vehicles using the KY-037 sensor. In this study, several stages were carried out including recognizing the problem, designing, manufacturing, and testing the tool. From the results of tests carried out by the smart traffic light system, it can prioritize intersections that experience vehicle compaction (miniature vehicles) and the KY-037 sensor can detect the sound of sirens so that it can prioritize vehicles that use sirens and obtain an accuracy rate of the tool in each experiment of 100% and the percentage level error 0%.