International Conference on Information Technology Applications and Systems (ICITAS), ICITAS2018

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Implementation Text Mining for Recommendation Follow Up Customer
Vivine Nurcahyawati

Last modified: 2018-05-11


—Customer is one of the biggest assets in a company. The cost of acquiring new customers is greater than the cost of maintaining customer relationships today. The company's followup should be appropriate to support customer retention. This study aims to produce applications as a tool to generate recommendations about customer conditions. In this article explained that used a combination of the concept of Mining Text and naïve bayes clasiffier algorithm to process the status of customers from social media, in this study using Facebook. After going through the testing phase, the application can generate recommendation data for follow-up on the customer.

Keywords—Data Mining, Customer Retention, Naïve Bayes Classifier, te I

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