Klasifikasi pengunjung mall menggunakan algoritma K-Means
Abstract
The impact of the pandemic on mall sales has made the characterization of mall visitors even more important to increase revenue. This study uses Particle Swarm Optimization (PSO) to optimize the K-Means approach to categorize mall visitors into several clusters. This study uses the Customer_mall Dataset from Kaggle, which is then processed using Python in Jupyter Notebook. Five clusters were created as a result of clustering, each of which describes a group of mall customers with a certain amount of wealth and spending. The results of this study, obtained a silhouette score of 0.553931997444648 which sufficiently indicates the best cluster, then the cluster results are analyzed to obtain customer segmentation based on the value of expenditure and income. the highest priority for the mall. The results from this study provide important information about marketing tactics that can be implemented to increase mall sales and improve understanding of mall consumer behavior.
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