The Implementation of K-Means dan K-Medoids Algorithm for Customer Segmentation on E-commerce Data Transactions

Abstract

Nowadays, e-commerce data transactions are commonly used by companies to provide new information. The data transaction can reveal customer segmentation or groups based on the similar characteristics and behavior of each customer. Data Mining is one of technique to conduct the customer segmentation through clustering method. The study aims to applied the clustering method on e-commerce data transactions by using both K-Means and K-Medoids algorithm. The result shows that both algorithms reveal optimum of cluster result with value of k = 3. The results are also indicating the conformity with the elbow method’s results and the Davies Bouldin Index validity test which shows that the optimal number of clusters is 3. The test results show that K-Medoids has the best performance with a ration value of 0.337575 compared to K-Means 0.3380724. Hence, K-Medoids are used in data clustering as the optimal cluster. The results of customer segmentation according to the Customer Loyalty Matrix consist of core customers, new customers, and lost customers.