Pengembalian Data Yang Hilang Pada Dataset Dengan Menggunakan Algoritma K-Nearest Neighbor Imputation Data Mining

Budianto Bangun, Abdul Karim Karim

Abstract


One of the things that is really hoped for when collecting data is to produce complete data. In research, incomplete data will affect the results obtained. because the process carried out in the research was not optimal. A dataset is a collection of information that is stored for a long time and becomes a large pile of data. Missing values in the dataset will be an important problem and must be handled in research. Therefore, data recovery is needed. Data mining is a process carried out in computer research. Where data mining will process data that has been collected first, either data collected by yourself (primary data) or data that has been collected in a dataset (secondary data). Recovery is the process of recovering data that is lost or cannot be found. The K-Nearest Neighbor Imputation algorithm is a system that uses a supervised learning algorithm and aims to discover new data patterns by connecting existing data patterns with new data. KNNI is an approach used to identify objects based on certain information, namely the closest distance to the object

Keywords


Return; Data; Lost; Datasets; K-Nearest Neighbor Imputation algorithm; Data Mining

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References


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DOI: https://doi.org/10.30865/mib.v8i3.8014

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