Agung Setia Prayudha; Rizaldy Khair
This study aims to analyze and implement the K-Nearest Neighbor (KNN) algorithm in determining the feasibility of sheep selection at PT Arjuna Farm. In the livestock industry, selecting quality animals is very important to increase productivity and efficiency. KNN, as a simple but effective classification method, is used to analyze sheep characteristic data, such as body weight, age, and health, to identify animals that meet the eligibility criteria. The research methodology used in this study includes data collection, pre-processing, and application of the KNN algorithm. Data obtained from PT Arjuna Farm were processed and divided into training data and testing data. The results of the analysis show that the KNN algorithm is able to provide high accuracy in determining the feasibility of sheep selection, with a low error rate.