Implementation Text Mining for Recommendation Follow Up Customer

Nurcahyawati, Vivine ORCID: (2018) Implementation Text Mining for Recommendation Follow Up Customer. In: Proceedings of International Conference on Information Technology Applications and Systems (ICITAS) 2018. Managing Digital Development for Sustainable Economy . Institut Bisnis dan Informatika Stikom Surabaya, Surabaya. ISBN 978-602-51367-0-2

3. Jurnal ICITAS.pdf - Published Version

Download (1MB) | Preview
2. Turn it in.pdf - Published Version

Download (862kB) | Preview
1. Reviewe.pdf - Published Version

Download (666kB) | Preview

Search this title on : |


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.

Export Record

Item Type: Book Section
Additional Information: Vivine Nurcahyawati
Uncontrolled Keywords: Data Mining, Customer Retention, Naïve Bayes, Classifier
Dewey Decimal Classification: 600 – Technology > 650 Management & auxiliary services > 658 General management
Divisions: Perpustakaan > Prosiding/Call for Papers
Depositing User: Annuh Liwan Nahar
Date Deposited: 18 Nov 2021 14:12
Last Modified: 25 May 2022 14:27

Download Statistics

Downloads over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Actions (login required)

View Item   View Item