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IJMLC 2020 Vol.10(2): 283-289 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.2.933

An Artificial Neural Network Approach for Predicting Customer Loyalty: A Case Study in an Online Travel Agency

M. Mujiya Ulkhaq, Arga Adyatama, Finsaria Fidiyanti, Riyan Rozaq, and M. Fauzan M. Raharjo

Abstract—The objective of this study is to predict the customer loyalty of an online travel agency (OTA) using the artificial neural network (ANN) approach. Six website quality dimensions, i.e., ease of use, security/privacy, information/ content, responsiveness, visual appeal, and fulfillment were used as independent variables to measure the service quality; while the dependent variables were represented by customers’ willingness to recommend the services, to revisit/reuse the services in the future, and to give positive referral to others. A case study was conducted in an Indonesian-based OTA. The results of the ANN then were compared with the logistic regression model. It was found that the ANN models can predict customer loyalty better than the logistic regression model as they have higher accuracy and lower root mean square error. This study is expected not only to give a contribution to the literature towards customers’ loyalty prediction but also to give an insight to the managers of OTA about how to pursue customer loyalty.

Index Terms—Artificial neural network, customer loyalty, logistic regression, online travel agency.

M. M. Ulkhaq, A. Adyatama, F. Fidiyanti, R. Rozaq, and M. F. M. Raharjo are with the Department of Industrial Engineering, Diponegoro University, Semarang 50275 Indonesia (e-mails: ulkhaq@live.undip.ac.id, adyatama.arga@gmail.com, finsafidi@gmail.com, riyan.rozaq@gmail.com, fauzanmarantama9@gmail.com).

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Cite: M. Mujiya Ulkhaq, Arga Adyatama, Finsaria Fidiyanti, Riyan Rozaq, and M. Fauzan M. Raharjo, "An Artificial Neural Network Approach for Predicting Customer Loyalty: A Case Study in an Online Travel Agency," International Journal of Machine Learning and Computing vol. 10, no. 2, pp. 283-289, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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