PREDICTION OF HOTEL BOOKING CANCELLATION USING DEEP NEURAL NETWORK AND LOGISTIC REGRESSION ALGORITHM
Abstract
Booking cancellation is a key aspect of hotel revenue management as it affects the room reservation system. Booking cancellation has a significant effect on revenue which has a significant impact on demand management decisions in the hotel industry. In order to reduce the cancellation effect, the hotel applies the cancellation model as the key to addressing this problem with the machine learning-based system developed. In this study, using a data collection from the Kaggle website with the name hotel-booking-demand dataset. The research objective was to see the performance of the deep neural network method which has two classification classes, namely cancel and not. Then optimized with optimizers and learning rate. And to see which attribute has the most role in determining the level of accuracy using the Logistic Regression algorithm. The results obtained are the Encoder-Decoder Layer by adamax optimizer which is higher than that of the Decoder-Encoder by adadelta optimizer. After adding the learning rate, the adamax accuracy for the encoders and encoders decreased for a learning rate of 0.001. The results of the top three ranks of each neural network after adding the learning rate show that the smaller the learning rate, the higher the accuracy, but we don't know what the optimal value for the learning rate is. By using the Logistic Regression algorithm by eliminating several attributes, the most influential level of accuracy is the state attribute and total_of_special_requests, where accuracy increases when the state attribute is removed because there are 177 variations in these attributes
References
Adi Putro, N., Septian, R., Widiastuti, Maulidah, M., & Ferdinandus Pardede, H. (2021). Penelitian Dosen Yayasan.
Afrianto, M. A., & Wasesa, M. (2020). Booking Prediction Models for Peer-to-peer Accommodation Listings using Logistics Regression, Decision Tree, K-Nearest Neighbor, and Random Forest Classifiers. Journal of Information Systems Engineering and Business Intelligence, 6(2), 123. https://doi.org/10.20473/jisebi.6.2.123-132
Aggarwal, R., & Ranganathan, P. (2017). Common pitfalls in statistical analysis: Linear regression analysis. Perspectives in Clinical Research, 8(2), 100–102. https://doi.org/10.4103/2229-3485.203040
Al‑Smadi, M., Talafha, B., Al‑Ayyoub, M., & Jararweh, Y. (2018). Using long short‑term memory deep neural networks for aspect‑based-al-smadi2018.pdf. https://doi.org/10.1007/s13042-018-0799-4
ALOTAIBI, E. (2020). Application of Machine Learning in the Hotel Industry: A Critical Review. Journal of Association of Arab Universities for Tourism and Hospitality, 0(0), 0–0. https://doi.org/10.21608/jaauth.2020.38784.1060
Antonio, N., Almeida, A. de, & Nunes, L. (2017). Predicting hotel booking cancellations to decrease uncertainty and increase revenue. Tourism & Management Studies, 13(2), 25–39. https://doi.org/10.18089/tms.2017.13203
Antonio, N., de Almeida, A., & Nunes, L. (2019). Hotel booking demand datasets. ELSEVIER, 22, 41–49. https://doi.org/10.1016/j.dib.2018.11.126
Antonio, N., De Almeida, A., & Nunes, L. (2017). Predicting hotel bookings cancellation with a machine learning classification model. Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017, 2017-Decem, 1049–1054. https://doi.org/10.1109/ICMLA.2017.00-11
Antonio, N., De Almeida, A., & Nunes, L. (2019). An automated machine learning based decision support system to predict hotel booking cancellations. Data Science Journal, 18(1), 1–20. https://doi.org/10.5334/dsj-2019-032
Kingma, D. P., & Ba, J. L. (2015). Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 1–15.
Kriegeskorte, N., & Golan, T. (2019). Neural network models and deep learning – a primer for biologists. ArXiv, 1–14.
Lee, M. (2020). a Machine Learning Approach To Improving Forecasting Accuracy of Hotel Demand: a Comparative Analysis of Neural Networks and Traditional Models. Issues In Information Systems, 21(1), 12–21. https://doi.org/10.48009/1_iis_2020_12-21
Leeuwen, R. van. (2018). Cancellation Predictor for Revenue Management applied in the hospitality industry.
Peng, C. Y. J., Lee, K. L., & Ingersoll, G. M. (2002). An introduction to logistic regression analysis and reporting. Journal of Educational Research, 96(1), 3–14. https://doi.org/10.1080/00220670209598786
Putra, J. W. G. (2019). Pengenalan Konsep Pembelajaran Mesin dan Deep Learning. 1–235.
Sánchez-Medina, A. J., & C-Sánchez, E. (2020). Using machine learning and big data for efficient forecasting of hotel booking cancellations. International Journal of Hospitality Management, 89, 102546. https://doi.org/10.1016/j.ijhm.2020.102546
Urraca, R., Sanz-Garcia, A., Fernandez-Ceniceros, J., Sodupe-Ortega, E., & Martinez-de-Pison, F. J. (2015). Improving Hotel Room Demand Forecasting with a Hybrid GA-SVR Methodology Based on Skewed Data Transformation, Feature Selection and Parsimony Tuning. In E. Onieva, I. Santos, E. Osaba, H. Quintián, & E. Corchado (Eds.), Hybrid Artificial Intelligent Systems (pp. 632–643). Cham: Springer International Publishing.
Wang, J., & Duggasani, A. (2020). Forecasting hotel reservations with long short-term memory-based recurrent neural networks. International Journal of Data Science and Analytics, 9(1), 77–94. https://doi.org/10.1007/s41060-018-0162-6
World Travel Organization. (2019). International Tourism Highlights : 2019 Edition. Unwto, 1–24. https://doi.org/10.18111/9789284421152
Copyright (c) 2021 Nugroho Adi Putro, Rendi Septian, Widiastuti Widiastuti, Mawadatul Maulidah, Hilman Ferdinandus Pardede
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The copyright of any article in the TECHNO Nusa Mandiri Journal is fully held by the author under the Creative Commons CC BY-NC license.
- The copyright in each article belongs to the author.
- Authors retain all their rights to published works, not limited to the rights set out on this page.
- The author acknowledges that Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) is the first to publish with a Creative Commons Attribution 4.0 International license (CC BY-NC).
- Authors can enter articles separately, manage non-exclusive distribution, from manuscripts that have been published in this journal into another version (for example: sent to author affiliation respository, publication into books, etc.), by acknowledging that the manuscript was published for the first time in Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri);
- The author guarantees that the original article, written by the stated author, has never been published before, does not contain any statements that violate the law, does not violate the rights of others, is subject to the copyright which is exclusively held by the author.
- If an article was prepared jointly by more than one author, each author submitting the manuscript warrants that he has been authorized by all co-authors to agree to copyright and license notices (agreements) on their behalf, and agrees to notify the co-authors of the terms of this policy. Techno Nusa Mandiri: Journal of Computing and Information Technology (TECHNO Nusa Mandiri) will not be held responsible for anything that may have occurred due to the author's internal disputes.
.png){kind=logo}
