DOI: https://doi.org/10.14710/jtsiskom.2020.13790

Prediksi pembatalan pemesanan hotel menggunakan optimalisasi hiperparameter pada algoritme Random Forest

Prediction of hotel bookings cancellation using hyperparameter optimization on Random Forest algorithm

Department of Informatics, Universitas Muhammadiyah Malang. Jl. Raya Tlogomas No.246, Malang, Jawa Timur 65144, Indonesia

Received: 14 Jun 2020; Revised: 16 Nov 2020; Accepted: 27 Nov 2020; Available online: 7 Dec 2020; Published: 31 Jan 2021.
Open Access Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract
Cancellation of hotel bookings by customers greatly influences hotel managerial decision making. To minimize losses by this problem, the hotel management made a fairly rigid policy that could damage the reputation and business performance. Therefore, this study focuses on solving these problems using machine learning algorithms. To get the best model performance, hyperparameter optimization is applied to the random forest algorithm. It aims to obtain the best combination of model parameters in predicting hotel booking cancellations. The proposed model is proven to have the best performance with the highest accuracy results of 87 %. This study's results can be used as a model component in hotel managerial decision-making systems related to future bookings' cancellation.
Fulltext View|Download Email colleagues
Keywords: classification; hyperparameter optimization; random forest
Funding: Universitas Muhammadiyah Malang, Indonesia

Article Metrics:

Article Info
Section: Original Research Articles
Language : ID
Related articles
Real-time currency recognition on video using AKAZE algorithm Face recognition system with PCA-GA algorithm for smart home door security using Rasberry Pi Discrimination of civet coffee using visible spectroscopy Automatic tsunami arrival detection algorithm for sea level observation system A proposed method for handling an imbalance data in classification of blood type based on Myers-Briggs type indicator More related articles
Most cited articles
Perancangan Papan Informasi Digital Berbasis Web pada Raspberry pi Perancangan dan Pembuatan Aplikasi Visualisasi 3D Interaktif Masjid Agung Jawa Tengah Menggunakan Unity3D Pengembangan Teknologi Informasi Mobile Learning Universitas Diponegoro Berbasis Android Mobile Application of Expert System for Meningitis Early Diagnosis Sistem Pengendali Peralatan Elektronik dalam Rumah secara Otomatis Menggunakan Sensor PIR, Sensor LM35, dan Sensor LDR More cited articles
  1. S. Kitamori, H. Sakai, and H. Sakaji, “Extraction of sentences concerning business performance forecast and economic forecast from summaries of financial statements by deep learning,” in IEEE Symposium Series on Computational Intelligence, Honolulu, USA, Dec. 2017, pp. 1-7. doi: 10.1109/SSCI.2017.8285335
  2. N. Antonio, A. de Almeida, and L. Nunes, “Predicting hotel bookings cancellation with a machine learning classification model,” in IEEE International Conference on Machine Learning and Applications, Cancun, Mexico, Dec. 2017, pp. 1049–1054. doi: 10.1109/ICMLA.2017.00-11
  3. N. Antonio, A. de Almeida, and L. Nunes, “Predicting hotel booking cancellations to decrease uncertainty and increase revenue,” Tourism & Management Studies, vol. 13, no. 2, pp. 25–39, 2017. doi: 10.18089/tms.2017.13203
  4. L. Rokach, “Decision forest: Twenty years of research,” Information Fusion, vol. 27, pp. 111–125, 2016. doi: 10.1016/j.inffus.2015.06.005
  5. P. Fernandez-Gonzalez, C. Bielza, and P. Larranaga, “Random forests for regression as a weighted sum of k-potential nearest neighbors,” IEEE Access, vol. 7, pp. 25660–25672, 2019. doi: 10.1109/ACCESS.2019.2900755
  6. M. C. M. Oo and T. Thein, “Hyperparameters optimization in scalable random forest for big data analytics,” in 4th International Conference on Computer and Communication Systems, Singapore, Singapore, Feb. 2019, pp. 125-129. doi: 10.1109/CCOMS.2019.8821752
  7. B. H. Shekar and G. Dagnew, “Grid search-based hyperparameter tuning and classification of microarray cancer data,” in International Conference on Advanced Computational and Communication Paradigms, Gantok, India, Feb. 2019, pp. 1-8. doi: 10.1109/ICACCP.2019.8882943
  8. T. Wang et al., “Random forest-bayesian optimization for product quality prediction with large-scale dimensions in process industrial cyber-physical systems,” IEEE Internet Things Journal, vol. 7, no. 9, pp. 8641-8653, 2020. doi: 10.1109/JIOT.2020.2992811
  9. E. Hazan, A. Klivans, and Y. Yuan, “Hyperparameter optimization: A spectral approach,” 2017, arXiv:1706.00764
  10. J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization,” Journal of Machine Learning Research, vol. 13, pp. 281-305, 2012
  11. N. Antonio, A. de Almeida, and L. Nunes, “Hotel booking demand datasets,” Data in Brief, vol. 22, pp. 41–49, 2019. doi: 10.1016/j.dib.2018.11.126
  12. C. Seger, “An investigation of categorical variable encoding techniques in machine learning: binary versus one-hot and feature hashing,” thesis, KTH Royal Insitute of Technology, Stockholm, Sweden, 2018
  13. J. S. Lee, “AUC4.5: AUC-based C4.5 decision tree algorithm for imbalanced data classification,” IEEE Access, vol. 7, pp. 106034 – 106042, 2019, doi: 10.1109/ACCESS.2019.2931865
  14. J. Li, S. Ma, T. Le, L. Liu, and J. Liu, “Causal decision trees,” IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 2, pp. 257–271, 2017. doi: 10.1109/TKDE.2016.2619350
  15. T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in International Conference on Knowledge Discovery and Data Mining, New York, USA, Aug. 2016, pp. 785-794. doi: 10.1145/2939672.2939785
  16. M. Chen, Q. Liu, S. Chen, Y. Liu, C. H. Zhang, and R. Liu, “XGBoost-Based algorithm interpretation and application on post-fault transient stability status prediction of power system,” IEEE Access, vol. 7, pp. 13149-13158, 2019. doi: 10.1109/ACCESS.2019.2893448
  17. N. Li, B. Li, and L. Gao, “Transient stability assessment of power system based on XGBoost and factorization machine,” IEEE Access, vol. 8, pp. 28403-28414,2020. doi: 10.1109/ACCESS.2020.2969446
  18. S. Georganos, T. Grippa, S. Vanhuysse, M. Lennert, M. Shimoni, and E. Wolff, “Very high resolution object-based land use-land cover urban classification using extreme gradient boosting,” IEEE Geoscience and Remote Sensing Letter, vol. 15, no. 4, pp. 607-611, 2018. doi: 10.1109/LGRS.2018.2803259
  19. D. Zhang, L. Qian, B. Mao, C. Huang, B. Huang, and Y. Si, “A data-driven design for fault detection of wind turbines using Random Forests and XGboost,” IEEE Access, vol. 6, pp. 21020–21031, 2018. doi: 10.1109/ACCESS.2018.2818678
  20. L. Breiman, “Random forests,” Machine Learning, vol. 45, pp. 5–32, 2001. doi: 10.1023/A:1010933404324
  21. S. Liu, H. Li, Y. Zhang, B. Zou, and J. Zhao, “Random forest-based track initiation method,” Journal of Engineering, vol. 2019, no. 19, pp. 6175-6179, 2019. doi: 10.1049/joe.2019.0180
  22. A. Primajaya and B. N. Sari, “Random Forest algorithm for prediction of precipitation,” Indonesian Journal of Artificial Intelligence and Data Mining, vol. 1, no. 1, pp. 27, 2018. doi: 10.24014/ijaidm.v1i1.4903
  23. D. Marinov and D. Karapetyan, “Hyperparameter optimisation with early termination of poor performers,” in Computer Science and Electronic Engineering, Colchester, UK, Sept. 2019, pp. 160–163. doi: 10.1109/CEEC47804.2019.8974317
  24. B. Nakisa, M. N. Rastgoo, A. Rakotonirainy, F. Maire, and V. Chandran, “Long short term memory hyperparameter optimization for a neural network based emotion recognition framework,” IEEE Access, vol. 6, pp. 49325–49338, 2018. doi: 10.1109/ACCESS.2018.2868361
  25. M. Feurer and F. Hutter, Hyperparameter Optimization. Springer, 2019. doi: 10.1007/978-3-030-05318-5_1

Last update:

  1. Proceedings of the 2023 3rd International Conference on Public Management and Intelligent Society (PMIS 2023)

    Ying Wang. Atlantis Highlights in Intelligent Systems, 8 , 2023. doi: 10.2991/978-94-6463-200-2_41

  2. Hotel Booking Cancelation Prediction using ML algorithms

    M. Venkata Rakesh, S. Prasanna Kumar, Yogitha, R Aishwarya.. 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS), 2022. doi: 10.1109/ICAIS53314.2022.9742843

  3. Rapid Identification of Tobacco Mildew Based on Random Forest Algorithm

    Zhimin Jiang, Wenjun Zhang, Haixia Huang, Zhengguang Zhai, Dairong chen, Yongfeng Ai, Bo Li, Xiaoxiang Chen, Lianhui Li. Scientific Programming, 2022 , 2022. doi: 10.1155/2022/1818398

  4. Dataset Outlier Detection Method Based on Random Forest Algorithm

    Yingzi Zheng. 2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM), 2022. doi: 10.1109/AIAM57466.2022.00111

  5. Online credit default prediction model based on fusion of Random Forest and XGBoost algorithm

    Yanhua Shen, Han Bao, Yanhui Feng, Yonghui Dai. 2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), 2023. doi: 10.1109/ICIBA56860.2023.10165530

  6. Equipment Damage Measurement Method of Wartime Based on FCE-PCA-RF

    Mingyu Li, Lu Gao, Hongwei Xu, Kai Li, Yisong Huang. Journal of Systems Engineering and Electronics, 35 (3), 2024. doi: 10.23919/JSEE.2024.000065

  7. Clinical Nursing Strategies for Pulmonary Disease Patients Based on Random Forest Algorithm

    Dai Yu, Ding Luping, He Lingmei, Wang Junfeng, Cui Dehua. 2023 International Conference on Electronics and Devices, Computational Science (ICEDCS), 2023. doi: 10.1109/ICEDCS60513.2023.00114

Last update: 2024-11-20 04:19:17

No citation recorded.