<![CDATA[Technological developments have changed conventional sales to online sales. In online sales, a review from a customer is a very important thing that needs attention, because customer reviews will show the quality and credibility of a sale. Customer reviews can increase or attract new customers or vice versa. Therefore reviews from customers need to be carried out sentiment analysis to know and understand, preferences and feelings of customers towards a product or service in online business. One of the analyzes that can be done is by clustering reviews from customers so that it can be seen from what side the customer dissatisfaction arose. In this study an analysis will be carried out by utilizing text mining from customer reviews by conducting clustering reviews using the K-means method. By grouping customer reviews, a model can be formed to classify the types of reviews according to their class. From the research conducted, it can be concluded that the k-means clustering method can be used to analyze customer sentiment grouping with the number of clusters produced there are 3 groups, namely in cluster 1 complaining about slow delivery, in cluster 2 it leads to a mismatch of goods ordered with goods received by customers, while the results of cluster 3 customer sentiment lead to service and packing. The results of this modeling can be used as a basis for making improvements in sales services at online stores.]]>
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