Optimizing Hotel Room Occupancy Prediction Using an Enhanced Linear Regression Algorithms
Keywords:
Hotel, Linear regression, Occupancy Prediction, Optimizing Model, Polynomial regression
Abstract
Predicting the correct hotel occupancy rate is important in the tourism industry because it has a major impact on the level of revenue and maintenance of a hotel’s reputation. With accurate predictions, hotel performance can be optimized regarding resources, staff, and hotel facilities. The linear regression method has been proven to perform causal predictions well. However, this method has several weaknesses, such as the function of the relationship between dependent variables and independent variables that are not linear, overfitting, or underfitting in building the prediction model. The purpose of this study was to optimize the linear regression model in predicting hotel occupancy rates. The method used in this study was a Linear Regression method optimized with Polynomial Regression and regularization techniques to reduce overfitting using Ridge Regression and Lasso Regression. The results of the model evaluation showed that linear regression, which was optimized with Polynomial Regression and Ridge Regression in the model with the historical data of the Adiwana Unagi occupancy rate, historical data of the hotel occupancy rate in Bali, and the number of tourist visits in Bali, gave the best performance, with a mean absolute error score of 1.0648, root mean square error of 2.1036, and R-squared of 0.9953. The conclusion of this research was optimization using polynomial regression, achieving the best evaluation scores, where the prediction model performance indicates that variable X7 (tourist visit numbers) strongly influences the prediction of the occupancy rate.
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Intelligence,” Materials Today: Proceedings, vol. 56, pp. 2252–2255, 2022, https://doi.org/10.1016/j.matpr.2021.11.577.
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a perspective article,” Tourism Review, vol. 75, no. 1, pp. 239–242, jan 2020, https://doi.org/10.1108/TR-06-2019-0224.
[4] A. Ampountolas and M. Legg, “Predicting daily hotel occupancy: a practical application for independent hotels,” Journal of
Revenue and Pricing Management, 2023, https://doi.org/10.1057/s41272-023-00445-7.
[5] D. Alita, A. D. Putra, and D. Darwis, “Analysis of classic assumption test and multiple linear regression coefficient test for
employee structural office recommendation,” IJCCS (Indonesian Journal of Computing and Cybernetics Systems), vol. 15,
no. 3, p. 295, 2021, https://doi.org/10.22146/ijccs.65586.
[6] K. Le Nguyen, H. Thi Trinh, T. T. Nguyen, and H. D. Nguyen, “Comparative study on the performance of different machine
learning techniques to predict the shear strength of RC deep beams: Model selection and industry implications,” Expert Systems
with Applications, vol. 230, p. 120649, 2023, https://doi.org/10.1016/j.eswa.2023.120649.
[7] S. Chakraborty, K. Kalita, R. Cep, and S. Chakraborty, “A Comparative Analysis on Prediction Performance of Regression
Models during Machining of Composite Materials,” materials, vol. 14, no. 6689, 2021, https://doi.org/doi:10.3390/
ma14216689.
[8] T. Setiyorini and T. Informatika, “Comparison Of Linear Regressions And Neural Networks For Forecasting Electricity Consumption,”
Pilar Nusa Mandiri, vol. 16, no. 2, pp. 135–140, 2020, https://doi.org/10.1016/j.ijepes.2014.12.036.
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on Predicting the Spread of Covid-19 in India,” in 2020 IEEE 5th International Conference on Computing Communication and
Automation (ICCCA), 2020, pp. 519–524, https://doi.org/10.1109/ICCCA49541.2020.9250748.
[10] Z. Zhou, C. Qiu, and Y. Zhang, “A comparative analysis of linear regression , neural networks and random forest regression
for predicting air ozone employing soft sensor models,” Scientific Reports, pp. 1–23, 2023, https://doi.org/10.1038/
s41598-023-49899-0.
[11] W. Kontar, S. Ahn, D. L. Mendoza, M. P. Buchert, J. C. Lin, S. Francisco, B. Area, E. M. Wells, and M. Small, “Bus Travel
Time Prediction : A Comparative Study of Linear and Non-Linear Machine Learning Models,” in AICECS 2021 Journal of
Physics: Conference Series, 2022, https://doi.org/10.1088/1742-6596/2161/1/012053.
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https://doi.org/10.34123/icdsos.v2021i1.199.
[15] G. Zhang, J. Wu, B. Pan, J. Li, M. Ma, M. Zhang, and J. Wang, “Improving daily occupancy forecasting accuracy for hotels
based on EEMD-ARIMA model,” Tourism Economics, vol. 23, no. 7, pp. 1496–1514, may 2017, https://doi.org/10.1177/
1354816617706852.
[16] Y. M. Chang, C. H. Chen, J. P. Lai, Y. L. Lin, and P. F. Pai, “Forecasting hotel room occupancy using long short-term memory
networks with sentiment analysis and scores of customer online reviews,” Applied Sciences (Switzerland), vol. 11, no. 21, 2021,
https://doi.org/10.3390/app112110291.
[17] M. Y. Anshori, T. Herlambang, V. Asyari, H. Arof, A. A. Firdaus, K. Oktafianto, and B. Suharto, “Optimization of Hotel W
Management through Performance Comparison of Support Vector Machine and Linear Regression Algorithm in Forecasting
Occupancy,” Nonlinear Dynamics and Systems Theory, vol. 24, no. 3, pp. 228–235, 2024.
[18] A. S. Akbar and R. H. Kusumodestoni, “Optimization of k value and lag parameter of k-nearest neighbor algorithm on the
prediction of hotel occupancy rates,” Jurnal Teknologi dan Sistem Komputer, vol. 8, no. 3, pp. 246–254, 2020, https://doi.org/
10.14710/jtsiskom.2020.13648.
[19] B. A. Abdelghani, A. A. Mohammad, J. Dari, M. Maleki, and S. Banitaan, “Occupancy Prediction: A Comparative Study of
Static and MOTIF Time Series Features UsingWiFi Syslog Data,” SSRN Electronic Journal, 2023, https://doi.org/10.2139/ssrn.
4452581.
[20] B. Economics, K. Kozlovskis, Y. Liu, N. Lace, and Y. Meng, “Application Of Machine Learning Algorithms To Predict Hotel
Occupancy,” Journal of Business Economics and Management, vol. 24, no. 3, pp. 594–613, 2023, https://doi.org/10.3846/jbem.
2023.19775.
[21] A. Derhab, A. Aldweesh, A. Z. Emam, and F. A. Khan, “Intrusion Detection System for Internet of Things Based on Temporal
Convolution Neural Network and Efficient Feature Engineering,” Wireless Communications and Mobile Computing, vol. 2020,
no. 1, p. 6689134, jan 2020, https://doi.org/10.1155/2020/6689134.
[22] P. S, “Understanding Polynomial Regression Model,” 2024.
[23] J. Kolluri, V. K. Kotte, M. S. B. Phridviraj, and S. Razia, “Reducing Overfitting Problem in Machine Learning Using Novel
L1/4 Regularization Method,” in 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184),
2020, pp. 934–938, https://doi.org/10.1109/ICOEI48184.2020.9142992.
[24] F. A. Rizalde, S. Mulyani, and N. Bachtiar, “Forecasting Hotel Occupancy Rate in Riau Province Using ARIMA and ARIMAX,”
in The 1’st International Conference on Data Science and Official Statistic, no. 25, 2021, pp. 578–589, https://doi.org/10.34123/
icdsos.v2021i1.199.
[4] K. Marzuki, A. Anggrawan, H. Wardhana, L. G. Rady Putra, and C. W. Rinaldi, “Design of Field Rental System on Web-Based
Garuda Mataram Badminton Club,” Jurnal Teknik Informatika, vol. 16, no. 1, pp. 58–68, 2023.
Published
2024-11-06
How to Cite
Pramita, D., Saraswati, N. W., Sandana, I. P., Pirozmand, P., & Bisena, I. K. (2024). Optimizing Hotel Room Occupancy Prediction Using an Enhanced Linear Regression Algorithms. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 24(1), 95-104. https://doi.org/https://doi.org/10.30812/matrik.v24i1.4254
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