Implementasi Deteksi Dini dan Klasifikasi Jenis Urine dengan Metode K-Nearest Neighbor (KNN) pada Pasien Operasi - Althaf Banafsaj Yudhistira; Rizal Maulana; Dahnial Syauqy (Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 2021)
Urine produced by each person may vary based on its physiological. It was happened by some reason like daily diet, gender, condition of exrectory system, and so on. These factor may lead physiological change of urine like color and turibidity. That is why urin often used to determine a person's health condition. In other side doing traditional urine analysis error often occurs because it only rely on analyzer sight. Analysis of a person's condition through urine physical conditions is also very much needed in the operation process and it is not possible if periodic analysis is carried out continuously during the operation. Therefore we need a tool that can perform automatic analysis to minimize errors in analysis and taking patient handling actions. This study uses the TCS3200 sensor to extract features in the form of color and an IR Proximity sensor for urine fluid turbidity. The two features will be processed by Arduino Uno to carry out the classification process. The urine will be divided into three classes, namely: Normal Urine, Blood Urine, Pus Urine. The classification process will use the K-Nearest Neighbor method with varying K values ​​starting from K = 3, K = 5, and K = 7. The system was able to recognize urine with an accuracy of 86.7% then 86.7% and 80% respectively