Corn is an agricultural crop in the Indonesian community, besides rice and soybeans because almost all of the area is fertile with planting seeds, the quality of corn quality that must be fulfilled as a food ingredient is very necessary for crop-producing farmers. The k-nearest neighbor algorithm is a method used to make predictions or classifications of objects based on training data that are the closest to the object or often called the euclidian distance. In this study used replace imputation for the preprocessing stage, missing value and baggin data are used to handle datasets in large scale while k-nearest neighbor is used as a classification of quality of corn quality based on attributes Variatas, Length, Shape, Taste Color, Seasonal Technique, Pest PH. . Based on the test data the best accuracy value is 79.30%, precision is 83.04% while recall with the value of 80.93% is obtained from the results of the performance test of bagging and replace imputation methods on the k-nearest neighbor algorithm with handling of missing value.