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: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org).
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).