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[Id] Local binary pattern adalah sebuah kode biner yang menggambarkan pola tekstur lokal. Hal ini dibangun dengan lingkungan batas dengan nilai abu-abu dari pusatnya. Local binary pattern tradisional memiliki beberapa kelemahan yakni varian terhadap rotasi dan pada saat proses thresholding pixel sensitif terhadap noise. Pada penelitian ini diusulkan sebuah metode ektraksi fitur baru untuk mengatasi masalah tersebut, metode tersebut disebut full neighbour local binary pattern (fnlbp). Metode ini nantinya akan dikombinasikan dengan discrete wavelet transform untuk ektraksi fitur dari citra mammogram dengan metode klasifikasi adalah Backpropagation Neural Network (BPNN). Berdasar ujicoba yang telah dilakukan metode usulan mendapatkan rata-rata akurasi yang lebih baik daripada metode local binary pattern tradisional baik yang dikombinasi dengan discrete wavelet transform ataupun tidak. Performa metode usulan full neighbour local binary pattern dapat menghasilkan akurasi yang sempurna yakni 100% baik pada saat menggunakan discrete wavelet transform ataupun tidak, sedangkan akurasi terendah yang didapat adalah 90.49%. Kata Kunci: Ekstraksi fitur, local binary pattern, wavelet , klasifikasi mammogram. ? [En] T raditional local binary pattern have some disadvantages which is a variant of the rotation and during the thresh old ing process the pixel is sensitive to noise. At this study the authors proposed a new method of features extraction to solve that problem and t his method called full neighbor local binary pattern (fnlbp). This method will be combined with discrete wavelet transform to extract the features of the mammogram image and the classification method is B ackpro - pagation Neural Network (BPNN). Based on experiments the result of proposed method in an average accuracy is better than traditional methods of local binary pattern which combined with discrete wavelet transform or not. The performance of the proposed method of full neighbor local binary pattern can produce perfect accuracy that is 100%, this accuracy is reached when using discrete wavelet transform or not, while the lowest accuracy obtained is 90.49%.

EKTRAKSI FITUR MENGGUNAKAN DISCRETE WAVELET TRANSFORM DAN FULL NEIGHBOUR LOCAL BINARY PATTERN UNTUK KLASIFIKASI MAMMOGRAM