The Occupancy Rate Modeling of Kendari Hotel Room using Mexican Hat Transformation and Partial Least Squares
Partial Least Squares (PLS) method was developed in 1960 by Herman Wold. The method particularly suits with construct a regression model when the number of independent variables is many and highly collinear. The PLS can be combined with other methods, one of which is a Continuous Wavelet Transformat...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Bina Nusantara University
2016-12-01
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| Series: | ComTech |
| Subjects: | |
| Online Access: | https://journal.binus.ac.id/index.php/comtech/article/view/3766 |
| _version_ | 1797762944533004288 |
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| author | Margaretha Ohyver Herena Pudjihastuti |
| author_facet | Margaretha Ohyver Herena Pudjihastuti |
| author_sort | Margaretha Ohyver |
| collection | DOAJ |
| description | Partial Least Squares (PLS) method was developed in 1960 by Herman Wold. The method particularly suits with construct a regression model when the number of independent variables is many and highly collinear. The PLS can be combined with other methods, one of which is a Continuous Wavelet Transformation (CWT). By considering that the presence of outliers can lead to a less reliable model, and this kind of transformation may be required at a stage of pre-processing, the data is free of noise or outliers. Based on the previous study, Kendari hotel room occupancy rate was affected by the outlier, and it had a low value of R2. Therefore, this research aimed to obtain a good model by combining the PLS method and CWT transformation using the Mexican Hats them other wavelet of CWT. The research concludes that merging the PLS and the Mexican Hat transformation has resulted in a better model compared to the model that combined the PLS and the Haar wavelet transformation as shown in the previous study. The research shows that by changing the mother of the wavelet, the value of R2 can be improved significantly. The result provides information on how to increase the value of R2. The other advantage is the information for hotel managements to notice the age of the hotel, the maximum rates, the facilities, and the number of rooms to increase the number of visitors. |
| first_indexed | 2024-03-12T19:35:31Z |
| format | Article |
| id | doaj.art-71d9dc00f83e4e998a0e233e72b0a769 |
| institution | Directory Open Access Journal |
| issn | 2087-1244 2476-907X |
| language | English |
| last_indexed | 2024-03-12T19:35:31Z |
| publishDate | 2016-12-01 |
| publisher | Bina Nusantara University |
| record_format | Article |
| series | ComTech |
| spelling | doaj.art-71d9dc00f83e4e998a0e233e72b0a7692023-08-02T04:13:31ZengBina Nusantara UniversityComTech2087-12442476-907X2016-12-017432533410.21512/comtech.v7i4.37663032The Occupancy Rate Modeling of Kendari Hotel Room using Mexican Hat Transformation and Partial Least SquaresMargaretha Ohyver0Herena Pudjihastuti1Bina Nusantara UniversityBina Nusantara UniversityPartial Least Squares (PLS) method was developed in 1960 by Herman Wold. The method particularly suits with construct a regression model when the number of independent variables is many and highly collinear. The PLS can be combined with other methods, one of which is a Continuous Wavelet Transformation (CWT). By considering that the presence of outliers can lead to a less reliable model, and this kind of transformation may be required at a stage of pre-processing, the data is free of noise or outliers. Based on the previous study, Kendari hotel room occupancy rate was affected by the outlier, and it had a low value of R2. Therefore, this research aimed to obtain a good model by combining the PLS method and CWT transformation using the Mexican Hats them other wavelet of CWT. The research concludes that merging the PLS and the Mexican Hat transformation has resulted in a better model compared to the model that combined the PLS and the Haar wavelet transformation as shown in the previous study. The research shows that by changing the mother of the wavelet, the value of R2 can be improved significantly. The result provides information on how to increase the value of R2. The other advantage is the information for hotel managements to notice the age of the hotel, the maximum rates, the facilities, and the number of rooms to increase the number of visitors.https://journal.binus.ac.id/index.php/comtech/article/view/3766occupancy rate modeling, hotel room modeling, Mexican hat, partial least squares |
| spellingShingle | Margaretha Ohyver Herena Pudjihastuti The Occupancy Rate Modeling of Kendari Hotel Room using Mexican Hat Transformation and Partial Least Squares ComTech occupancy rate modeling, hotel room modeling, Mexican hat, partial least squares |
| title | The Occupancy Rate Modeling of Kendari Hotel Room using Mexican Hat Transformation and Partial Least Squares |
| title_full | The Occupancy Rate Modeling of Kendari Hotel Room using Mexican Hat Transformation and Partial Least Squares |
| title_fullStr | The Occupancy Rate Modeling of Kendari Hotel Room using Mexican Hat Transformation and Partial Least Squares |
| title_full_unstemmed | The Occupancy Rate Modeling of Kendari Hotel Room using Mexican Hat Transformation and Partial Least Squares |
| title_short | The Occupancy Rate Modeling of Kendari Hotel Room using Mexican Hat Transformation and Partial Least Squares |
| title_sort | occupancy rate modeling of kendari hotel room using mexican hat transformation and partial least squares |
| topic | occupancy rate modeling, hotel room modeling, Mexican hat, partial least squares |
| url | https://journal.binus.ac.id/index.php/comtech/article/view/3766 |
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