Alat Pengklasifikasi Status Burung Puyuh Berdasarkan Berat Badan Menggunakan Metode K-NN Pada Embeded Sistem - Boris Wiyan Pradana; Hurriyatul Fitriyah; Rizal Maulana (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2019)
Every year,Quail can producing eggs from 240 until 300 grain each head. With their small body, the result of their egg's production are high enough. For breeder raising quail is a good opportunity to make a profit with the many demands nowadays. The method that used by breeder to improve the quality of quails is to separate the cage according to the age of the quails. Separating cages is also useful for distinguishing feed, and quail's nutrition that needed. In this study, we will use body weight as a parameter to classify the status of quail growth, which in general farmers use age as a parameter to separate cages. Loadcell weight sensors will be used as a medium to measure bird weight. Then Arduino will be used as a microcontroller and LCD to display the results. In classifying quail based in body weight, it requires a method to calculate them. Method that used in this study is the K-nearest neighbor(KNN) method. This system will test several asppects of the system including sensor accuracy, comparison between age and weight classification, and system computation time. For the accuracy of the sensor we got the result of accuracy 96.67%. Then in testing using KNN based on body weight compared to age, get the result of an error of 2.23%. And for testing computation time, obtained an average time of 612ms.