Klasifikasi pengunjung mall menggunakan algoritma K-Means

Authors

  • Kartika Wulandari Program Studi Teknik Informatika, Universitas Islam Negeri Maulana Malik Ibrahim Malang

Keywords:

Clustering; K-Means; Eblow; Silhouette Efficient; Customers Mall; Particle Swarm Optimization

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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Published

2023-11-30

How to Cite

Wulandari, K. (2023). Klasifikasi pengunjung mall menggunakan algoritma K-Means. Maliki Interdisciplinary Journal, 1(5), 564–573. Retrieved from https://urj.uin-malang.ac.id/index.php/mij/article/view/4621

Issue

Vol. 1 No. 5 (2023): NOVEMBER

Section

Articles

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