Improving Algorithm Accuracy K-Nearest Neighbor Using Z-Score Normalization and Particle Swarm Optimization to Predict Customer Churn

Due to increased competition in the business world, many companies use data mining techniques to determine the loyalty level of customers. In this business, data mining can be used to determine the loyalty level of customers. Data mining consists of several research models, one of which is classification. One of the most commonly used methods in classification is the K-Nearest Neighbor algorithm. In this study, the data which used are from German Credit Datasets obtained from UCI machine learning repository. The purpose of this study is to find out how Z-Score works to normalize the data and Particle Swarm Optimization to find the most optimal K value parameters, so the performance of the K-Nearest Neighbor algorithm is more optimal during the classification.  The methods which were used to normalize the data are Z-score and Particle Swarm Optimization to determine the most optimal K value. The classification was tested using confusion matrix to determine the generated accuracy. From the finding of this study, the application of Z-score normalization and Particle Swarm Optimization with the K Nearest Neighbor algorithm succeed in increasing the accuracy up to 14%. The initial accuracy was 68.5%, and after applying the normalization of Z-Score and Particle Swarm Optimization, the accuracy became 82.5%.