23 research outputs found
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%
Pendeteksian Outlier pada Model Regresi Ganda: Studi Kasus Tingkat Penghunian Kamar Hotel di Kendari
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