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Ohyver, Margaretha
Bina Nusantara University
Computer Science & IT Engineering Mathematics

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The Occupancy Rate Modeling of Kendari Hotel Room using Mexican Hat Transformation and Partial Least Squares Ohyver, Margaretha; Pudjihastuti, Herena
ComTech: Computer, Mathematics and Engineering Applications Vol 7, No 4 (2016): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v7i4.3766

Abstract

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.
Metode Regresi Ridge Untuk Mengatasi Kasus Multikolinear Ohyver, Margaretha
ComTech: Computer, Mathematics and Engineering Applications Vol 2, No 1 (2011): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v2i1.2782

Abstract

Multicolinear is a case that occurs in multi-linear regression analysis. Using multicolinear, it will be difficult to separate the influence of each independent variable towards the response variables. It also occurs in a farm production like cabbage. To solve this problem, Ridge regression method is used. This research aims to obtain a Ridge regression model to solve the multicolinear case. By using this method, the alleged regression coefficient is obtained by variance inflation factor less than ten for six free variables. 
Pemodelan Principal Component Regression dengan Software R Ohyver, Margaretha
ComTech: Computer, Mathematics and Engineering Applications Vol 3, No 1 (2012): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v3i1.2400

Abstract

Principal Component Regression (PCR) is one method to handle multicollinear problems. PCR produces principal components that have a VIF less than ten. The purpose for this research is to obtained PCR model using R software. The result is a model of PCR with two principal components and determination coefficients R(square) = 97,27%.
Pemodelan Tingkat Penghunian Kamar Hotel di Kendari dengan Transformasi Wavelet Kontinu dan Partial Least Squares Ohyver, Margaretha; Pudjihastuti, Herena
ComTech: Computer, Mathematics and Engineering Applications Vol 5, No 2 (2014): ComTech
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/comtech.v5i2.2435

Abstract

Multicollinearity and outliers are the common problems when estimating regression model.   Multicollinearitiy occurs when there are high correlations among predictor variables, leading to difficulties in separating the effects of each independent variable on the response variable. While, if outliers are present in the data to be analyzed, then the assumption of normality in the regression will be violated and the results of the analysis may be incorrect or misleading. Both of these cases occurred in the data on room occupancy rate of hotels in Kendari. The purpose of this study is to find a model for the data that is free of multicollinearity and outliers and to determine the factors that affect the level of room occupancy hotels in Kendari. The method used is Continuous Wavelet Transformation and Partial Least Squares. The result of this research is a regression model that is free of multicollinearity and a  pattern of data that resolved the present of outliers.
Co-Authors Pudjihastuti, Herena