Prediksi Customer Churn dengan Algoritma Decision Tree C4.5 Berdasarkan Segmentasi Pelanggan untuk Mempertahankan Pelanggan pada Perusahaan Retail
Customer is a very important asset for retail companies. This is the reason why retail companies should plan and use a fairly clear strategy in treating customers. With the large number of customers, the problem that must be faced is how to identify the characteristics of all customers and able to retain existing customers in order not to stop buying and moving to a competitor retail company. By applying the concept of CRM, a company can identify customers by segmenting customers while also being able to implement customer retention programs by predicting potential churn on each customer class. The data used comes from UD.Mawar Sari. Customer segmentation process uses RFM model to get customer class. UD. Mawar Sari customer class is dormant, everyday, golden and superstar. The construction of prediction models using the Decision Tree C4.5. The application of the prediction model obtains performance results, that is: Dormant: Recall 97.51%, Precision 75.18%, Accuracy 76.18%. Everyday: Recall 100%, Precision 99.04%, Accuracy 99.04%. Golden: Recall 100%, Precision 98.84%, Accuracy 98.84%. Superstar: Recall 96.15%, Precision 99.43%, Accuracy 95.63%. Results of the evaluation with confusion matrix it can be concluded that the dormant customer class is a potentially churn customer class.
 A. Chorianopoulus, Effective CRM using Predictive Analytics. Wiley, 2009.
 E. C. Murphy and M. A. Murphy, Leading On the Edge Of Chaosâ€¯: The 10 Critical Elements for Success in Volatile Times. USA: Prentice Hall Press, 2002.
 E. Prasetyo, Data Miningâ€¯: Konsep dan Aplikasi Menggunakan Matlab. Yogyakarta: Penerbit ANDI Yogyakarta, 2013.
 E. Prasetyo, Data Mining Mengolah Data Menjadi Informasi Menggunakan Matlab. Yogyakarta: Penerbit ANDI Yogyakarta, 2014.
 F. Gorunescu, Data Mining Concept Model and Techniques. Berlin: Springer, 2011.
 G. Klepac, Developing Churn Models Using Data mining Techniques and Social Network Analysis. USA: IGI Global, 2015.
 J. Burez and D. Van den Poel, â€œHandling class imbalance in customer churn prediction,â€ Expert Syst. Appl., vol. 36, no. 3 PART 1, pp. 4626â€“4636, 2009.
 J. Han and M. Kamber, Data Miningâ€¯: Concepts and Techniques, 2nd ed. Morgan Kaufmann Publishers, 2006.
 K. W. Wong, â€œData Mining Using Fuzzy Theory for Customer Relationship Management,â€ vol. 4, no. Wawisr, pp. 188â€“200, 2001.
 M. Listiana, Sudjalwo, and D. Gunawan, â€œPerbandingan Algoritma Decision Tree (C4.5) Dan NaÃ¯ve Bayes Pada Data Mining Untuk Identifikasi Tumbuh Kembang Anak Balita (Studi Kasus Puskesmas Kartasura),â€ Informatika, vol. 1, no. 1, p. 18, 2015.
 P. . Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining. Pearson Education, Inc, 2006.
 P. S. Venatesan, Data Mining and Warehousing. New Age International (P) Limited, 2007.
 R. Govindaraju, T. Simatupang, and T. A. Samadhi, â€œPerancangan Sistem Prediksi Churn Pelanggan,â€ Tek.
Inform., vol. 9, no. 1, pp. 33â€“42, 2008.
 V. L. M. Oliviera, â€œAnalytical Customer Relationship Management in Retailing Supported by Data Mining Techniques,â€ University of Porto, 2012.
 V. L. M. Oliviera, â€œPredicting Partial Customer Churn using Markov for Discrimination for Modeling First Purchase Sequence,â€ University of Porto, 2012.
 W. Buckinx and D. Van Den Poel, â€œCustomer base analysis: Partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting,â€ Eur. J. Oper. Res., vol. 164, no. 1, pp. 252â€“268, 2005.
 Y. Liu and Y. Zhuang, â€œResearch Model of Churn Prediction Based on Customer Segmentation and Misclassification Cost in the Context of Big Data,â€ J. Comput. Commun., vol. 3, no. 3, pp. 87â€“93, 2015.
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