ALGORITMA K-MEANS UNTUK PENGELOMPOKAN PERILAKU CUSTOMER
Abstract
In the rapidly evolving digital era, understanding customer purchasing behavior is crucial for marketing strategies and business development. This study uses the K-means clustering algorithm to analyze and segment customer purchasing behavior. This algorithm effectively partitions data into groups based on similar characteristics. The aim of this study is to identify purchasing behavior patterns using attributes such as purchase frequency, expenditure amount, and product types. By segmenting customers into homogeneous groups, companies can design more effective marketing strategies and better personalization. The results show that the K-means clustering method successfully segments customers based on similar behavior patterns, which can be used for market segmentation and strategy development. The application of this algorithm in purchasing behavior analysis is expected to provide deep insights and support better business decision-making, offering a competitive advantage for companies.
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References
Clayman, C. L., Srinivasan, S. M., & Sangwan, R. S. (2020). K-means clustering and principal components analysis of microarray data of L1000 landmark genes. Procedia Computer Science, 97-104. https://doi.org/10.1016/j.procs.2020.02.265
Kanungo, T., et al. (n.d.). An Efficient k-Means Clustering Algorithm: Analysis and Implementation.
Guo, Z., Shi, Y., Huang, F., Fan, X., & Huang, J. (2021). Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management. Geoscience Frontiers, 12(6). https://doi.org/10.1016/j.gsf.2021.101249
Gustriansyah, R., Suhandi, N., & Antony, F. (2019). Clustering optimization in RFM analysis based on k-means. Indonesian Journal of Electrical Engineering and Computer Science, 18(1), 470-477. https://doi.org/10.11591/ijeecs.v18.i1.pp470-477
Xie, H., et al. (2019). Improving K-means clustering with enhanced Firefly Algorithms. Applied Soft Computing Journal, 84, 105763. https://doi.org/10.1016/j.asoc.2019.105763
Xie, H., et al. (2019). Improving K-means clustering with enhanced Firefly Algorithms. Applied Soft Computing Journal, 84. https://doi.org/10.1016/j.asoc.2019.105763
Boone, T., Ganeshan, R., Jain, A., & Sanders, N. R. (2019). Forecasting sales in the supply chain: Consumer analytics in the big data era. International Journal of Forecasting, 35(1), 170-180. https://doi.org/10.1016/j.ijforecast.2018.09.003
Boone, T., Ganeshan, R., Jain, A., & Sanders, N. R. (2019). Forecasting sales in the supply chain: Consumer analytics in the big data era. International Journal of Forecasting, 35(1), 170-180. https://doi.org/10.1016/j.ijforecast.2018.09.003
Sarasvananda, I. B. G., Wardoyo, R., & Sari, A. K. (2019). The K-Means Clustering Algorithm With Semantic Similarity To Estimate The Cost of Hospitalization. IJCCS (Indonesian Journal of Computing and Cybernetics Systems, 13(4), 313. https://doi.org/10.22146/ijccs.45093
Sharma, V., & Bala, M. (2020). An Improved Task Allocation Strategy in Cloud using Modified K-means Clustering Technique. Egyptian Informatics Journal, 21(4), 201-208. https://doi.org/10.1016/j.eij.2020.02.001
Kotary, D. K., & Nanda, S. J. (2019). Automatic Determination of K in Distributed K-Means Clustering. Procedia Computer Science, 556-564. https://doi.org/10.1016/j.procs.2020.01.050
Mohamad, I. B., & Usman, D. (2013). Standardization and its effects on K-means clustering algorithm. Research Journal of Applied Sciences, Engineering and Technology, 6(17), 3299-3303. https://doi.org/10.19026/rjaset.6.3638
Patel, E., & Kushwaha, D. S. (2020). Clustering Cloud Workloads: K-Means vs Gaussian Mixture Model. Procedia Computer Science, 158-167. https://doi.org/10.1016/j.procs.2020.04.017
Sinaga, K. P., & Yang, M. S. (2020). Unsupervised K-means clustering algorithm. IEEE Access, 8, 80716-80727. https://doi.org/10.1109/ACCESS.2020.2988796
Makwana, P., Kodinariya, T. M., & Makwana, P. R. (2013). Review on Determining of Cluster in K-means Clustering Review on determining number of Cluster in K-Means Clustering. International Journal of Advance Research in Computer Science and Management Studies, 1(6). Retrieved from https://www.researchgate.net/publication/313554124
Oyelade, O. J., Oladipupo, O. O., & Obagbuwa, I. C. (2010). Application of k-Means Clustering algorithm for prediction of Students’ Academic Performance. Retrieved from http://sites.google.com/site/ijcsis/
Shirazi, F., & Mohammadi, M. (2019). A big data analytics model for customer churn prediction in the retiree segment. International Journal of Information Management, 48, 238-253. https://doi.org/10.1016/j.ijinfomgt.2018.10.005
Wu, J., et al. (2020). An Empirical Study on Customer Segmentation by Purchase Behaviors Using a RFM Model and K-Means Algorithm. Mathematical Problems in Engineering, 2020. https://doi.org/10.1155/2020/8884227
Park, E., Kang, J., Choi, D., & Han, J. (2020). Understanding customers’ hotel revisiting behaviour: a sentiment analysis of online feedback reviews. Current Issues in Tourism, 23(5), 605-611. https://doi.org/10.1080/13683500.2018.1549025
Cheng, L. C., Wu, C. C., & Chen, C. Y. (2019). Behavior analysis of customer churn for a customer relationship system: An empirical case study. Journal of Global Information Management, 27(1), 111-127. https://doi.org/10.4018/JGIM.2019010106
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