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

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

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 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

Obstacles Avoidance For Intelligent Telepresence Robot Using Interval Type-2 FLC

Abstract. Intelligent Telepresence robot is a new trend for communication remotely today, and obstacles avoidance for robot is one of the important research areas. This research reports and presents obstacles avoidance method for intelligent telepresence robot, a custom-build robot system specifically designed for teleconference with multiple people. We propose an interval type-2 FLC (Fuzzy Logic Controller) that is able to handle uncertainties for measuring distance of obstacle to navigate the robot. The robot is controlled using computer networks, so the manager/supervisor at office/industry can direct the robot to the intended person to start a discussion/inspection. We build a web application for controlling the multi-client telepresence robot and open-source video conference system. Experimental result shows the ability of robot to be controlled remotely and to avoidobstacles smoothly and we evaluated its performance

METODE REGRESI RIDGE UNTUK MENGATASI KASUS MULTIKOLINEAR

Multikolinear merupakan salah satu kasus yang terjadi dalam analisis regresi linear ganda. Dengan adanya multikolinear, akan sulit memisahkan pengaruh masing-masing variabel bebas terhadap variabel respon. Kasus ini pun terjadi pada hasil produksi usaha tani kol bulat. Untuk mengatasi kasus ini, digunakan metode regresi Ridge. Tujuan penelitian ini adalah memperoleh model regresi Ridge yang dapat mengatasi kasus multikolinear. Berdasarkan metode ini diperoleh koefisien regresi dugaan dengan variance inflation factor yang kurang dari sepuluh untuk keenam variabel bebas

PENERAPAN PARTIAL LEAST SQUARES PADA DATA GINGEROL

Model multivariate calibration bertujuan untuk menduga ukuran-ukuran yang mahal diperoleh dengan menggunakan ukuran-ukuran yang murah dan mudah. Ada beberapa masalah yang sering terjadi pada pemodelan kalibrasi, diantaranya dan multikolinear. Untuk mengatasi permasalahan tersebut maka digunakan metode partial least squares (PLS). Penelitian dilakukan untuk menerapkan metode PLS pada data gingerol. Berdasarkan penelitian yang dilakukan diperoleh model dengan 2 komponen dengan keragaman peubah Y sebesar 83,8032% dan keragaman peubah X sebesar 100% serta diperoleh untuk R2 = 83,8% dan RMSE = 0,100891 kelompok data kalibrasi dan R2 = 84,2% dan RMSEP = 0,199939 untuk kelompok data validasi

PENERAPAN METODE TRANSFORMASI LOGARITMA NATURAL DAN PARTIAL LEAST SQUARES UNTUK MEMPEROLEH MODEL BEBAS MULTIKOLINIER DAN OUTLIER

Multicollinear and outlier occur when making regression modeling. Multicollinear leads difficulty in separating the influence of each independent variable on the response variable. Outlier causes unmet assumption of normality in the regression. Both cases occur in the number of hotel visitors in Kendari. The purpose of this paper is to find a model that is free from multicollinear and outlier. Using the natural logarithm transformation and partial least squares, obtained model has the value of variance inflation factor less than ten and is able to overcome the outlier

PEMODELAN PRINCIPAL COMPONENT REGRESSION DENGAN SOFTWARE R

Principal Component Regression (PCR) merupakan salah satu metode yang dapat digunakan untuk mengatasi masalah multikolinear. PCR menghasilkan komponen-komponen utama yang memiliki VIF kurang dari sepuluh. Tujuan dari penelitian ini adalah untuk memperoleh model PCR dari data yang mengandung multikolinear dengan bantuan software R. Hasil yang diperoleh adalah model PCR dengan dua komponen utama dan koefisien determinasi R 97,27%

PEMODELAN TINGKAT PENGHUNIAN KAMAR HOTEL DI KENDARI DENGAN TRANSFORMASI WAVELET KONTINU DAN PARTIAL LEAST SQUARES

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