Comparison of k-nearest neighbor and neural network for forecasting occupancy rate at Hotel XYZ
The occupancy rate of a hotel is an important factor to see the development of providers business performance. By forecasting occupancy rate, the hotel can identify business opportunities or adjust room prices, determine hotel operations, and take this into consideration for strategic decision makin...
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| Language: | en |
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InforMath Publishing Group
2025
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| Online Access: | http://eprints.utem.edu.my/id/eprint/29338/2/0076501102025.pdf http://eprints.utem.edu.my/id/eprint/29338/ https://www.e-ndst.kiev.ua/v25n4/3(100)a.pdf |
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| author | Anshori, Mohamad Yusak Katias, Puspandam Herlambang, Teguh Azmi, Mohd Sanusi Othman, Zuraini Oktafianto, Kresna |
| author_facet | Anshori, Mohamad Yusak Katias, Puspandam Herlambang, Teguh Azmi, Mohd Sanusi Othman, Zuraini Oktafianto, Kresna |
| author_sort | Anshori, Mohamad Yusak |
| building | UTEM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknikal Malaysia Melaka |
| content_source | UTEM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | The occupancy rate of a hotel is an important factor to see the development of providers business performance. By forecasting occupancy rate, the hotel can identify business opportunities or adjust room prices, determine hotel operations, and take this into consideration for strategic decision making. In this study, occupancy rate forecasting for Hotel XYZ was carried out by comparing the k-nearest
neighbor (k-NN) and neural network methods. The dataset used in this study included rooms available, rooms sold out, and available occupancy percentage data in Hotel XYZ from April 2018 to June 2023. The simulation was carried out by dividing the data into training data and testing data with a ratio of 70:30 and 80:20. Model creation was carried out by applying the k-NN and neural network methods to the
Hotel XYZ data set. Forecasting results that were obtained using k-NN showed an optimal RMSE at 70%:30% split of data with an RMSE of 0.080 at k-value 3, while forecasting results obtained using the neural network showed an optimal RMSE at 70%:30% data split with an RMSE of 0.007 for two hidden layers. The comparison of results of forecasting by k-NN and neural network showed an optimal RMSE when using neural network method with an RMSE of 0.004, a GAP of 0.076 compared to using k-NN. The results of this study can be used by Hotel XYZ to make better decisions in determining hotel policies in the future and goals set by the hotel. |
| format | Article |
| id | my.utem.eprints-29338 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2025 |
| publisher | InforMath Publishing Group |
| record_format | eprints |
| spelling | my.utem.eprints-293382025-12-30T04:11:43Z http://eprints.utem.edu.my/id/eprint/29338/ Comparison of k-nearest neighbor and neural network for forecasting occupancy rate at Hotel XYZ Anshori, Mohamad Yusak Katias, Puspandam Herlambang, Teguh Azmi, Mohd Sanusi Othman, Zuraini Oktafianto, Kresna The occupancy rate of a hotel is an important factor to see the development of providers business performance. By forecasting occupancy rate, the hotel can identify business opportunities or adjust room prices, determine hotel operations, and take this into consideration for strategic decision making. In this study, occupancy rate forecasting for Hotel XYZ was carried out by comparing the k-nearest neighbor (k-NN) and neural network methods. The dataset used in this study included rooms available, rooms sold out, and available occupancy percentage data in Hotel XYZ from April 2018 to June 2023. The simulation was carried out by dividing the data into training data and testing data with a ratio of 70:30 and 80:20. Model creation was carried out by applying the k-NN and neural network methods to the Hotel XYZ data set. Forecasting results that were obtained using k-NN showed an optimal RMSE at 70%:30% split of data with an RMSE of 0.080 at k-value 3, while forecasting results obtained using the neural network showed an optimal RMSE at 70%:30% data split with an RMSE of 0.007 for two hidden layers. The comparison of results of forecasting by k-NN and neural network showed an optimal RMSE when using neural network method with an RMSE of 0.004, a GAP of 0.076 compared to using k-NN. The results of this study can be used by Hotel XYZ to make better decisions in determining hotel policies in the future and goals set by the hotel. InforMath Publishing Group 2025 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/29338/2/0076501102025.pdf Anshori, Mohamad Yusak and Katias, Puspandam and Herlambang, Teguh and Azmi, Mohd Sanusi and Othman, Zuraini and Oktafianto, Kresna (2025) Comparison of k-nearest neighbor and neural network for forecasting occupancy rate at Hotel XYZ. Nonlinear Dynamics and Systems Theory, 25 (4). pp. 373-385. ISSN 1562-8353 https://www.e-ndst.kiev.ua/v25n4/3(100)a.pdf |
| spellingShingle | Anshori, Mohamad Yusak Katias, Puspandam Herlambang, Teguh Azmi, Mohd Sanusi Othman, Zuraini Oktafianto, Kresna Comparison of k-nearest neighbor and neural network for forecasting occupancy rate at Hotel XYZ |
| title | Comparison of k-nearest neighbor and neural network for forecasting occupancy rate at Hotel XYZ |
| title_full | Comparison of k-nearest neighbor and neural network for forecasting occupancy rate at Hotel XYZ |
| title_fullStr | Comparison of k-nearest neighbor and neural network for forecasting occupancy rate at Hotel XYZ |
| title_full_unstemmed | Comparison of k-nearest neighbor and neural network for forecasting occupancy rate at Hotel XYZ |
| title_short | Comparison of k-nearest neighbor and neural network for forecasting occupancy rate at Hotel XYZ |
| title_sort | comparison of k-nearest neighbor and neural network for forecasting occupancy rate at hotel xyz |
| url | http://eprints.utem.edu.my/id/eprint/29338/2/0076501102025.pdf http://eprints.utem.edu.my/id/eprint/29338/ https://www.e-ndst.kiev.ua/v25n4/3(100)a.pdf |
| url_provider | http://eprints.utem.edu.my/ |