Pengenalan karakter plat nomor kendaraan menggunakan extreme learning machine

Putra, Chrystia Aji (2015) Pengenalan karakter plat nomor kendaraan menggunakan extreme learning machine. Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Plat nomor kendaraan adalah salah satu jenis tanda identifikasi kendaraan bermotor. Pengenalan karakter merupakan salah satu tahap dalam sistem pengenalan plat nomor. Pengenalan karakter dilakukan untuk mendapatkan data karakter teks. Metode baru dari Jaringan Syaraf Tiruan(JST) diusulkan untuk pengenalan karakter pada penelitian ini. Metode yang diusulkan adalah Extreme Learning Machine (ELM). ELM merupakan jaringan syaraf tiruan feedforward dengan satu hidden layer. ELM lebih dikenal dengan istilah single hidden layer feedforward neural network (SLFNs). Metode ELM mempunyai kelebihan dalam learning speed. Penelitian ini menyelesaikan langkah pengenalan karakter huruf dan angka pada citra plat nomor. Data karakter didapatkan menggunakan vertical and horizontal projection. Pengujian pada penelitin ini menggunakan data plat nomor yang berlaku di Indonesia. Uji coba dilakukan dengan menggunakan 40 citra plat nomor. Terdapat 273 karakter dalam 40 data plat nomer tersebut. Pengujian dilakukan pada 273 data karakter. Pengujian dilakukan dengan membandingkan performa dua buah metode. Dua metode tersebut adalah ELM dan Neural Network (NN). Pelatihan menggunakan ELM menunjukan waktu pelatihan yang lebih cepat daripada NN. Pelatihan menggunakan ELM membutuhkan lama waktu 11 detik. Pelatihan menggunakan NN membutuhkan waktu 144 detik. Akurasi pengujian ELM dan NN menunjukan hasil yang sama yaitu 78,75%. ============================================================================================== Vehicle license plate is a type of vehicle identification. Character recognition is one step in a vehicle license recognition system. Character recognition is performed to obtain a text character data. Character recognition in this research proposed a new method of Artificial Neural Network (ANN). The proposed method was Extreme Learning Machine (ELM). ELM is a feed-forward neural network with one hidden layer. ELM is better known as single hidden layer feed-forward neural network (SLFNs). ELM has the advantage in speed learning. Character data was obtained using vertical horizontal projection. Testing on this research was done by using vehicle license plates prevailing in Indonesia. Testing was done by using 40 images of vehicle license plates. There were 273 characters in 40 images of vehicle license plates. Testing data was conducted on 273 characters. Testing was done by comparing two methods. These methods were ELM and Neural Network (NN). ELM showed faster training time than that of NN. Training using ELM took 11 seconds, while training using NN took 144 seconds. The accuracy of ELM and NN showed the same result, which was 78.75%.

Item Type: Thesis (Masters)
Additional Information: RTE 006.42 Put p
Uncontrolled Keywords: Extreme learning machine; pengenalan karakter; plat nomor kendaraan
Subjects: Q Science > Q Science (General) > Q337.5 Pattern recognition systems
Divisions: Faculty of Electrical Technology > Electrical Engineering > 20101-(S2) Master Thesis
Depositing User: - Taufiq Rahmanu
Date Deposited: 28 Oct 2019 03:50
Last Modified: 28 Oct 2019 03:50
URI: http://repository.its.ac.id/id/eprint/71444

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