PT. Micro Madani Institute is a company engaged in the training and recruitment of employees, the company is given the mandate to recruit Mekaar employees. In 2018 along with the rapid growth of the Mekaar Branch Office from 1,100 units to 2,500 units, the number of HR management will automatically increase by 27,500 people. With this increase in HR management targets, PT. Micro Madani Institute takes a strategic step to accelerate employee fulfillment. The level of fulfillment and resignation of employees is highly considered by the company especially with the number of employee resignations every year continues to increase constantly hencethe process of achieving targets for employee fulfillment is hampered as many employees resigns. One way to deal with this is to implement data mining to assist in decision making. In this study the author implemented the K-Nearest Neighbor method to assist analysts in predicting employee resignation rates. The K-NN analysis process in this study uses 4 variables: position, distance traveled, years of service, and resignation reason. The study applied the euclidian distance to calculate the distance between training data and test data sorted according to the smallest distance value. The study used the 5 nearest neighbors (k) namely 1,3,5,7 and 11 which were used to see the cluster patterns of the neighbors. Reduction of employee resignation analysis implemented with the K-Nearest Neighbor method can generate resign reasoning patterns from the results of euclidian distance. The results expected to facilitate the company's management in the process of analyzing the rate of employee reesignation by examining previous data in order to create the right decision in minimizing employee resignations.

Data Mining Employee Resignation K-Nearest.