OPTIMALISASI KLASIFIKASI KENDARAAN OTOMATIS DENGAN SISTEM KONTROL ARAH KENDARAAN BERBASIS SENSOR INFRAMERAH
Keywords:inframerah sensor, vehicle, classification
Before using the Automatic Toll Gate (GTO) on toll roads, an officer entered the vehicle type data manually on the transaction machine to issue toll rates according to the type of vehicle. After the use of the GTO, the officer's job was replaced with an Automatic Vehicle Classification (AVC) electronic device to automatically classify the types of vehicles so that toll rates could be issued according to the type of vehicle. To solve this problem, the AVC device is added with an inframerah sensor as a control system for the direction of a vehicle that is moving backwards. This additional inframerah sensor consists of a transmitter and receiver module to detect the direction of the vehicle, which is installed at a distance of 50 cm from the profile scanner sensor. From the results of the tests carried out, the results of the sample transaction data show that the maximum error percentage before the addition of an inframerah sensor is 0.91% and the maximum error percentage after adding an inframerah sensor is 0.54% so that there is a decrease in error by 0.37%. Thus, optimization of automatic vehicle classification can be achieved.
Peraturan Pemerintah Republik Indonesia Nomor 15 Tahun 2005 Tentang Jalan Tol Pasal 1 Available:
Kapsch TrafficCom. "Automatic Vehicle Classification (AVC)" Available: https://www.kapsch.net/ktc/downloads
Baskara, “Dasar Teori ATMega16” Available: http://baskarapunya.blogspot.com/2012/09/dasar-teori-atmega16.html
Bayu Tenoyo, "Spesifikasi Sistem Automatic Vehicle Classification Menggunakan B-Method", Fakultas Ilmu Komputer Universitas Indonesia Depok 2011.
Firsa Hari, "Prinsip Kerja Piezoelectric". Available: https://www.scribd.com/document/408 500404/Prinsip-Kerja-Piezoelectric-1
Taufiq Dwi Septian Suyadhi. 2014.
Phototransistor. Robotics University