Pengenalan Angka Bahasa Isyarat dengan Menggunakan Local Directional Pattern dan Klasifikasi K-Nearest Neighbour
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Abstract
Dengan semakin berkembangnya teknologi, diharapkan System Pengenalan Bahasa Isyarat juga semakin berkembang. Sistem Pengenalan Angka Bahasa Isyarat ini dapat dilakukan dengan pembelajaran mesin. Pada penelitian ini dijelaskan tentang sistem pengenalan citra angka Bahasa isyarat dengan menggunakan metode ekstraksi ciri Local Directional Pattern dan Klasifikasi K-Nearest Neighbour. Sistem ini terdiri dari beberapa tahap, yaitu pengumpulan data, pre-processing, ekstraksi ciri dan klasifikasi. Pengujian system ini menggunakan data Turkey Ankara Ayrancı Anadolu High School's Sign Language Digits Dataset sebanyak 2.062 data. Pengujian pada sistem pengenalan angka tulisan tangan ini menunjukkan bahwa metode Local Directional Pattern dapat mengenali angka Bahasa Isyarat hingga mencapai akurasi 88.45% dengan pembagian region pada citra hingga 81 region, dan mengambil 3 tetangga terdekat pada tahap klasifikasi K-Nearest Neighbour. Penelitian ini dilakukan untuk mengetahui parameter terbaik yang digunakan dalam metode Local Directional Pattern dan klasifikasi k-Nearest Neighbor.
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