Abstract
Bali cattle is an Indonesian native cattle that have a characteristic of the color of his skin. Bali cattle skin color can indicate the quality of the Bali cattle. The classification of the quality of Bali cattle directly is difficult because the human eye has a limited ability to see colors. A decision support system that is able to classify the quality of Bali cattle is based on a digital image of the skin color can help to overcome these limitations. The system will classify Bali cattle into three classes, namely Good (Seeds Superior), Average and Poor. System applying the K-Nearest Neighbor algorithm for the classification process is based on the average features and standard deviation of the red, green, and blue (RGB). This research tested a method to obtain the best value of K, the best image size, and the amount of training data best that will be used. Male Bali cattle using a value of K = 3, image size = 128×128 pixel, and the amount of training data = 45. While the female Bali cattle using a value of K = 6, image size = 64×64 pixel, and the amount of training data = 30. The results of testing the accuracy of the system for male Bali cattle is 100%, while the results of testing the accuracy of the system for female Bali cattle is 66.67%.
Highlights
Peternakan di Indonesia terus melakukan upaya dalam pengembangbiakan ternaknya
an Indonesian native cattle that have a characteristic of the color of his skin
Bali cattle skin color can indicate the quality of the Bali cattle
Summary
Peternakan di Indonesia terus melakukan upaya dalam pengembangbiakan ternaknya. Pengupayaan tersebut didasari pada semakin lemahnya produktifitas dan perkembangan ternak sapi di Indonesia (Soekardono dkk, 2009). SPK tersebut melakukan klasifikasi dengan menggunakan fitur dari citra sapi Bali yaitu, nilai warna red, green, dan blue (RGB). Penulis menyimpulkan bahwa metode yang akan digunakan dalam proses pengklasifikasian penelitian ini adalah metode KNN. Penentuan metode dari algoritma KNN adalah menggunakan perhitungan jarak Euclidean dan menggunakan variabel mean dan standar deviasi komponen warna RGB dari citra warna kulit sapi Bali. Berdasarkan permasalahan yang telah dijelaskan diatas dan melihat beberapa penelitian sebelumnya, maka judul yang diusulkan dalam penelitian ini adalah “Sistem Pendukung Keputusan Pemilihan Bibit Unggul Sapi Bali Berdasarkan Warna Kulit Menggunakan Metode K-Nearest Neighbor”
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