Optimizing Hotel Room Occupancy Prediction Using an Enhanced Linear Regression Algorithms
DOI:
https://doi.org/10.30812/matrik.v24i1.4254Keywords:
Hotel, Linear regression, Occupancy Prediction, Optimizing Model, Polynomial regressionAbstract
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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Revenue and Pricing Management, 2023, https://doi.org/10.1057/s41272-023-00445-7.
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Models during Machining of Composite Materials,†materials, vol. 14, no. 6689, 2021, https://doi.org/doi:10.3390/
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Automation (ICCCA), 2020, pp. 519–524, https://doi.org/10.1109/ICCCA49541.2020.9250748.
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s41598-023-49899-0.
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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.
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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.
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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,â€
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.
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2024-11-06
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D. A. K. Pramita, N. W. S. Saraswati, I. P. D. Sandana, P. Pirozmand, and I. K. A. Bisena, “Optimizing Hotel Room Occupancy Prediction Using an Enhanced Linear Regression Algorithms”, MATRIK, vol. 24, no. 1, pp. 95–104, Nov. 2024, doi: 10.30812/matrik.v24i1.4254.
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