DOI of the published article https://doi.org/10.51519/journalisi.v6i1.666
Dynamic Segmentation Analysis for Expedition Services: Integrating K-Means and Decision Tree
Analisis Segmentasi Dinamis Untuk Layanan Ekspedisi: Mengintegrasikan K-Means dan Decision Tree
DOI:
https://doi.org/10.21070/ups.4438Keywords:
K-Means Clustering, Decision Tree, Expedition Services, MarketplaceAbstract
Technological developments have an inpact on increasing the level of competition between companies in acquiring and retaining customers. With this competition, companies must maximise efforts to reach consumers and understand customer service needs so that the business can continue to survive and experience development. In this effort, The data processing was done using rapidminer with k-means clustering and decision tree methods. The research results show that k-means clustering achieved the lowest Davies Bouldin Index (DBI) accuracy results, namely -0,943 in cluster_8. In the research using the decision tree methods, accuracy results were obtained at 49.83%, with the good cluster being cluster_7. In this case, better accuracy values can be achieved by using the k-means clustering methods. This research can provide an illustration of the importance of utulizing the k-means and decision tree algorithm in classifying sales data as a tool for optimizing marketing and service efforts.
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