AKURASI ALAT PENGHITUNG LALU LINTAS PLATO 2.1 BERBASIS PENGOLAHAN CITRA - BACKGROUND SUBSTRACTION (ACCURACY OF TRAFFIC COUNTER PLATO 2.1 BASED ON IMAGE PROCESSING - BACKGROUND SUBSTRACTION)
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Abstract
ABSTRAK Penempatan sensor fisik di dalam lapisan perkerasan jalan sudah tidak memungkinkan lagi untuk diterapkan mengingat banyaknya kendaraan berat yang melintas di ruas jalan dan kegiatan overlay yang menyebabkan sensor mudah tidak berfungsi. PLATO 2.1 merupakan teknologi pengolahan citra video yang dikembangkan di Pusat Litbang Jalan dan Jembatan menggunakan metode background substraction. Penelitian ini bermaksud untuk mengetahui akurasi PLATO 2.1 dalam penghitungan volume lalu lintas. Metode yang dilakukan adalah membandingkan data volume lalu lintas yang dihitung secara manual dengan data yang dihitung menggunakan PLATO 2.1. Selanjutnya algoritma dalam PLATO 2.1 dimodifikasi dan digunakan untuk menghitung volume lalu lintas. Data volume lalu lintas yang dihasilkan dibandingkan terhadap data volume lalu lintas manual. Hasil penelitian menunjukkan bahwa perbedaan penghitungan kendaraan secara manual dan PLATO 2.1 adalah 30% untuk lalu lintas normal dan 3% untuk lalu lintas sedang. Setelah dilakukan modifikasi pada algoritma, yaitu dengan memisahkan algoritma pendeteksian motor dengan mobil dan mengganti metode area counting dari dua menjadi tiga area, ternyata dapat menghasilkan penghitungan yang lebih baik. Perbedaan penghitungan kendaraan PLATO 2.1 dan modifikasi PLATO 2.1 adalah 3% untuk lalu lintas normal dan 5% untuk lalu lintas sedang.
Kata kunci: volume lalu lintas, background substraction, modifikasi algoritma, alat penghitung volume lalu lintas, pengolahan citra video
ABSTRACT Placement of physical sensors in the pavement layer is no longer possible to apply given the many heavy vehicles that cross the road and overlay activities that cause the sensor easily does not work. PLATO 2.1 is a video image processing technology developed at IRE using the background substraction method. This research intends to know the accuracy of PLATO 2.1 in calculating traffic volume. The method used is to compare the traffic volume data calculated manually with the data calculated using PLATO 2.1. The next algorithm in PLATO 2.1 is modified and used to calculate the volume of traffic. The resulting traffic volume data is then compared against the traffic volume data manually. The results showed that the difference in vehicle count manually and PLATO 2.1 is 30% for normal traffic and 3% for medium traffic. After modification of the algorithm, separating the motor detection algorithm by car and changing the counting area method from two to three, it can produce better calculation. The difference in the calculation of the PLATO 2.1 vehicle and the modification of PLATO 2.1 is 3% for normal traffic and 5% for medium traffic.
Keywords: traffic volume, background substraction, algorithm modification, traffic counters, video image processing
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