K-Nearest Neighbor method for detecting egg quality conditions using Raspberry Pi

Food production in Indonesia is very strong and constrained, such as rice, corn, eggs and so on. Eggs that are consumed every day by Indonesian citizens, many consumers of egg food are not alert to the condition or condition of the eggs, whether they are feasible or expired. There are many studies related to the nature of eggs, egg quality and others, in part there is no modeling tool for detecting egg quality conditions based on raspberry pi using the K-Nearest Neighbor method which can be used to see good or bad eggs. In this study, the Raspberry Pi was used as the main component with the aid of a camera as an image capture on eggs using training data of 100 egg samples consisting of 50 good eggs and 50 bad eggs in order to get maximum accuracy. With the help of 2 led as a light source and the software uses the Python language. The purpose of this study was to obtain the expected accuracy by collecting 20 eggs randomly to get a percentage (17/20) * 100 = 85%.