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

Authors

  • Margaretha Ohyver Bina Nusantara University
  • Herena Pudjihastuti Bina Nusantara University

DOI:

https://doi.org/10.21512/comtech.v5i2.2435

Keywords:

multicollinearity, outlier, partial least squares, wavelet

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.

Dimensions

Plum Analytics

References

Abdi, H. (2003). Partial Least Squares (KTP) Regression. Encyclopedia of Social Sciences Research Methods (online), 1-7. Diakses dari www.utdallas.edu/~herve.

Addison, P. (2002). The Illustrated Wavelet Transform Handbook. Institute of Physics Publishing, London.

Antoniadis, A., Bigos, J., dan Sapatinas, T. (2001). Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study. Journal of Statistical Software, 6, hal. 1-83.

Biro Humas Pemerintahan Provinsi Sulawesi Tenggara. (2012). Profil Sultra.

Boulesteix, A., Strimmer, K. (2006). Partial Least Squares: A Versatile Tool For The Analysis Of High-Dimensional Genomic Data. Diakses dari http://www.slcmsr.net/boulesteix/papers/review

Chen, D., Hu, B., X, Shao., Qingde, S. (2004). Variable Selection by Modified IPW (Iterative Predictor Weighting)-KTP (Partial Least Squares) in Continuous Wavelet Regression Models. The Analyst. Diakses dari www.rsc.org/analyst.

Derquenne, C. (1993). Outlier detection before running statistical methods. Theory of Probability and its Applications, 37(2), 323-4. doi:10.1137/1137066.

Daubechies, I. (1992). Ten Lectures on Wavelets. CBMS-NSF Regional Conference Series in Applied Mathematics, 61, Philadelphia, PA: SIAM.

Garthwaite, P. H. (1994). An Interpretation of Partial Least Squares. Journal of the American Statistical Association, 89: 122-127.

Investor Daily Indonesia. (2012). Pertumbuhan Ekonomi Sultra Lampaui Nasional. Diakses dari http://www.investor.co.id/home/pertumbuhan-ekonomi-sultra-lampaui-nasional/28618

Katalog BPS. (2011). Direktori dan Tingkat Penghunian Kamar Hotel Provinsi Sulawesi Tenggara Tahun 2011. Kendari: Badan Pusat Statistik Provinsi Sulawesi Tenggara.

Lang, W. C., Forinash, K. (1998). Time-Frequency Analysis with the Continuous Wavelet Transform. Am. J. Phys, 66, 794-797.

Media Sultra. (2011). Sektor Perhotelan Sultra Serap 8791 Tenaga Kerja. Diakses dari http://sindikasi.inilah.com/read/detail/1812732/sektor-perhotelan-sultra-serap-8791-tenaga-kerja.

Murguia, J. S., Campos-Canton, E. (2005). Wavelet Analysis of Chaotic Time Series. Revista Mexicana De Fisica, 52(2), 155-162.

Naes, T., Isaksson, T., Fearn, T., dan Davies, T. (2002). Multivariate Calibration and Classification. Chichester: NIR Publications.

Zhu, C., Kitagawa, H., Papadimitriou, S., Faloutsos, C. (2011). Outlier detection by example. Journal of Intelligent Information Systems, 36(2), 217-247. doi:10.1007/s10844-010-0128-1.

Downloads

Published

2014-12-01

Issue

Section

Articles
Abstract 344  .
PDF downloaded 221  .