FORECASTING THE OCCUPANCY RATE OF STAR HOTELS IN BALI USING THE XGBOOST AND SVR METHODS

Aviolla Terza Damaliana(1*), Amri Muhaimin(2), Dwi Arman Prasetya(3)


(1) Data Science, Faculty of Computer Science, UPNVJT
(2) Data Science, Faculty of Computer Science, UPNVJT
(3) Data Science, Faculty of Computer Science, UPNVJT
(*) Corresponding Author

Abstract


The hotel occupancy rate indicator has become a concern in recent years as it goes hand in hand with the rapid growth of the global tourism industry. A way to maintain or even improve this indicator is to carry out managerial planning using forecasting methods. The forecasting methods used in this research are XGBoost and SVR. The advantage of this modelling is that it achieves high accuracy and processing speed. Meanwhile, the benefit of SVR is that it will produce good prediction because can overcome overfitting. The steps in this research are exploring data, separating training data and testing data, transforming data, modelling data, forecasting data, and evaluating forecasting results using RMSE, MAE, and MAPE. The results show that MAPE value from both methods is smaller than 10%, which means that both methods can predict the occupancy rate of star hotels in Bali very accurately. Apart from that, the SVR method has smaller values for all model evaluation criteria than the XGBoost method, which means that the SVR method is better than XGBoost for predicting the occupancy rate of star hotels in Bali.

Keywords


forecasting; SVR; XGBoost

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References


N. AISHAH, D. DEVIANTO, and M. MAIYASTRI, “PEMODELAN JUMLAH KUNJUNGAN WISATAWAN MANCANEGARA KE INDONESIA MELAUI BANDARA NGURAH RAI BALI DENGAN MODEL SARIMA-ARCH,” Jurnal Matematika UNAND, vol. 10, no. 3, p. 248, Jul. 2021, doi: 10.25077/jmu.10.3.248-259.2021.

B. G. Prianda and E. Widodo, “PERBANDINGAN METODE SEASONAL ARIMA DAN EXTREME LEARNING MACHINE PADA PERAMALAN JUMLAH WISATAWAN MANCANEGARA KE BALI,” BAREKENG: Jurnal Ilmu Matematika dan Terapan, vol. 15, no. 4, pp. 639–650, Dec. 2021, doi: 10.30598/barekengvol15iss4pp639-650.

N. U. Clarissa, W. Sulandari, and R. Respatiwulan, “Peramalan jumlah kedatangan wisatawan mancanegara ke bali menggunakan metode hibrida SSA-WFTS,” Jurnal Ilmiah Matematika, vol. 8, no. 1, p. 19, Apr. 2021, doi: 10.26555/konvergensi.v8i1.21460.

H. Christian Anderson Wint, A. Irma Purnama, and T. Suprapti, “PREDIKSI HUNIAN HOTEL MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBORS (STUDI KASUS : HOTEL RUMAH KITA KOTA CIREBON),” 2024.

R. N. Darmawan, J. C. A. Wijaya, and A. P. Putra, “Peramalan Tingkat Penghunian Kamar (TPK) pada Hotel Berbintang di Banyuwangi dengan Metode Naive dan Decomposition,” G-Tech: Jurnal Teknologi Terapan, vol. 8, no. 1, pp. 114–124, Dec. 2023, doi: 10.33379/gtech.v8i1.3543.

J. Dwi Putra Tamasoleng, I. Bagus Ary Indra Iswara, J. Tukad Pakerisan No, and P. Denpasar Selatan, “Analisis Perbandingan Metode Triple Exponential Smoothing dan Metode Winter Untuk Peramalan Tingkat Hunian Hotel Aston Denpasar,” Jurnal Nasional Komputasi dan Teknologi Informasi, vol. 3, no. 1, 2020.

L. Zhang, W. Bian, W. Qu, L. Tuo, and Y. Wang, “Time series forecast of sales volume based on XGBoost,” in Journal of Physics: Conference Series, IOP Publishing Ltd, Apr. 2021. doi: 10.1088/1742-6596/1873/1/012067.

F. Yulianto, W. Firdaus Mahmudy, and A. A. Soebroto, “Comparison of Regression, Support Vector Regression (SVR), and SVR-Particle Swarm Optimization (PSO) for Rainfall Forecasting,” 2020. [Online]. Available: www.jitecs.ub.ac.id

C. X. Lv, S. Y. An, B. J. Qiao, and W. Wu, “Time series analysis of hemorrhagic fever with renal syndrome in mainland China by using an XGBoost forecasting model,” BMC Infect Dis, vol. 21, no. 1, Dec. 2021, doi: 10.1186/s12879-021-06503-y.

Md. S. Rahman, A. H. Chowdhury, and M. Amrin, “Accuracy comparison of ARIMA and XGBoost forecasting models in predicting the incidence of COVID-19 in Bangladesh,” PLOS Global Public Health, vol. 2, no. 5, p. e0000495, May 2022, doi: 10.1371/journal.pgph.0000495.

H. Oukhouya and K. El Himdi, “Comparing Machine Learning Methods—SVR, XGBoost, LSTM, and MLP— For Forecasting the Moroccan Stock Market,” MDPI AG, Jun. 2023, p. 39. doi: 10.3390/iocma2023-14409.

D. Indra Purnama and S. Setianingsih, “Support Vector Regression (SVR) Model for Forecasting Number of Passengers on Domestic Flights at Sultan Hasanudin Airport Makassar Model Support Vector Regression (SVR) untuk Peramalan Jumlah Penumpang Penerbangan Domestik di Bandara Sultan Hasanudin Makassar,” vol. 16, no. 3, pp. 391–403, 2020, doi: 10.20956/jmsk.v%vi%i.9176.

I. Nengah Dharma Pradnyandita and A. A. Rohmawati, “Electronic Money Transactions Forecasting with Support Vector Regression (SVR) and Vector Autoregressive Moving Average (VARMA),” Intl. Journal on ICT, vol. 8, no. 1, pp. 69–85, 2022, doi: 10.21108/ijoict.v8i1.632.

Z. Meng, H. Sun, and X. Wang, “Forecasting Energy Consumption Based on SVR and Markov Model: A Case Study of China,” Front Environ Sci, vol. 10, Apr. 2022, doi: 10.3389/fenvs.2022.883711.

L. Rubio and K. Alba, “Forecasting Selected Colombian Shares Using a Hybrid ARIMA-SVR Model,” Mathematics, vol. 10, no. 13, Jul. 2022, doi: 10.3390/math10132181.

T. Trimono, A. Sonhaji, and U. Mukhaiyar, “FORECASTING FARMER EXCHANGE RATE IN CENTRAL JAVA PROVINCE USING VECTOR INTEGRATED MOVING AVERAGE,” MEDIA STATISTIKA, vol. 13, no. 2, pp. 182–193, Dec. 2020, doi: 10.14710/medstat.13.2.182-193.


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DOI: https://doi.org/10.26714/jsunimus.12.1.2024.24-33

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