The application of the K-NN imputation method for handling missing values in a dataset

Abstract

One of the things that is highly expected when collecting data is to produce complete data. In research, incomplete data will affect the results obtained. This is due to the non-maximum process carried out in the research. A dataset is a collection of data information that has been stored for a long time and becomes a large pile of data. Not infrequently, the dataset used in the research data presented is not complete. The missing value problem can be solved using data mining techniques. Data mining is the process of extracting information from a collection of data already stored in the data warehouse. Classification is the process of finding a common identity among different entities and classifying them into appropriate classes. Classification of large and complex data if performed manually would be difficult and take a long time. The K-Nearest Neighbor Imputation algorithm is a system that uses a supervised learning algorithm and aims to find new data patterns by connecting existing data patterns with new data. The conclusion that the authors drew is that the application of data mining to search for lost data using the K-Nearset Neighbor Imputation (KNNI) method is a process for generating new knowledge in the form of a comparison between the factors that influence the search for the missing data. The results of data mining using the K-Nearest Neighbor Imputation (KNNI) method are an arrangement of sequences of activities that support each other in the process.