Pemodelan Tingkat Penghunian Kamar Hotel  di Kendari dengan Transformasi Wavelet Kontinu dan Partial Least Squares

Ohyver, Margaretha; Pudjihastuti, Herena

doi:10.21512/comtech.v5i2.2435

Cited by 1 publication

(4 citation statements)

References 9 publications

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“…Fourth, the model resulted by using the wavelet transformation withMexican Hat and the Partial Least Square method is a significant model. The R 2 of the model is 73,76 %, and it is better than what Ohyver and Pudjihastuti (2014) have achieved.…”

Section: Discussionmentioning

confidence: 52%

“…The result is quite bad because the value of R 2 is only 34%. In the previous study by Ohyver and Pudjihastuti (2014), the model resulted was in Equation ( 13). The model is obtained by using the Haar wavelet on the scale of 6.…”

Section: Figure 3 the Number Of Hotels In Southeast Sulawesimentioning

confidence: 99%

“…This research is made to apply the CWT and the PLS methods into the hospitality data. The data used is hotel visitor data in Southeast Sulawesi Province that have been used in the previous study by Ohyver and Pudjihastuti (2014). The previous study has applied the combined methods of PLS and CWT with the Haar wavelet in 1-5 scalesas them other wavelet.…”

Section: Introductionmentioning

confidence: 99%

“…The low value of R 2 can be solved by constructing the model using available variables. Ohyver and Pudjihastuti (2014) constructed the model using CWT as the pre-processing stage combined with PLS as the modeling stage. They stated that one of the causes of the low value of R 2 was a discrepancy of used mother wavelet.…”

Section: Introductionmentioning

confidence: 99%

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The Occupancy Rate Modeling of Kendari Hotel Room using Mexican Hat Transformation and Partial Least Squares

Ohyver

Pudjihastuti

2016

ComTech

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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.

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Section: Discussionmentioning

confidence: 52%

Section: Figure 3 the Number Of Hotels In Southeast Sulawesimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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