Maximum Likelihood Classification dengan Ekstraksi Fitur Fast Fourier Transform untuk Pengenalan Mobil

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Article Info
Submitted: 2017-12-03
Published: 2018-01-31
Section: Articles
Language: ID

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The car recognition is part of the field of traffic surveillance on the image. In general, the car recognition using the form-based feature as a unique feature. Another feature in object recognition is the frequency feature. One feature of frequency is the Fourier feature, this feature is obtained by using Fast Fourier Transform (FFT) method. The object recognition can be done by determining the maximum value of likelihood and classifying it with Maximum Likelihood Classification (MLC). The use of FFT and MLC in the car object recognition has never been used. The results of both are in a good accuracy that is 76%.

Pengenalan mobil merupakan bagian dari bidang pengamatan lalu lintas pada citra. Umumnya pengenalan mobil menggunakan fitur bentuk sebagai fitur unik. Fitur lain dalam pengenalan objek adalah fitur frekuensi. Salah satu fitur frekuensi adalah fitur fourier, fitur ini didapat dengan menggunakan metode Fast Fourier Transform (FFT). Pengenalan objek dapat dilakukan dengan cara menentukan nilai maksimum likelihood dan mengklasifikasikannya dengan Maximum Likelihood Classification (MLC). Penggunaan FFT dan MLC dalam pengenalan objek mobil pada citra belum pernah digunakan. Hasil dari penggunaan keduanya mengghasilkan akurasi yang baik yaitu 76%.


Fast fourier transform; maximum likelihood classification; car recognition


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  1. Derry Alamsyah  Scholar
    Program Studi Teknik Informatika, STMIK Global Informatika MDP Palembang, Indonesia