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                        PENDEKATAN REGRESI NONPARAMETRIK DENGAN MENGGUNAKAN ESTIMATOR KERNEL PADA DATA KURS RUPIAH TERHADAP DOLAR AMERIKA SERIKAT 
                    
                    DEWA AYU DWI ASTUTI; 
I GUSTI AYU MADE SRINADI; 
MADE SUSILAWATI                    
                     E-Jurnal Matematika Vol 7 No 4 (2018) 
                    
                    Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University 
                    
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                                DOI: 10.24843/MTK.2018.v07.i04.p218                            
                                            
                    
                        
                            
                            
                                
Nonparametric regression can be applied for some data types one of them is time series data. The technique of this method is called smoothing technique. There are several smoothing techniques however this study used kernel estimator with seven kernel functions in data of rupiah exchange rate to US dollar. The analysis with R shows that by using minimum Generalized Cross Validation (GCV) criteria, seven functions produce various optimal bandwidth value but has similar curves estimation. The conclusion is that by using kernel estimator in time series data support that choosing the optimal bandwidth is more important than choosing the kernel functions.
                            
                         
                     
                 
                
                            
                    
                        PENERAPAN BOOTSTRAP DALAM METODE MINIMUM COVARIANCE DETERMINANT (MCD) DAN LEAST MEDIAN OF SQUARES (LMS) PADA ANALISIS REGRESI LINIER BERGANDA 
                    
                    NI PUTU IIN VINNY DAYANTI; 
NI LUH PUTU SUCIPTAWATI; 
MADE SUSILAWATI                    
                     E-Jurnal Matematika Vol 5 No 1 (2016) 
                    
                    Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University 
                    
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                                DOI: 10.24843/MTK.2016.v05.i01.p116                            
                                            
                    
                        
                            
                            
                                
Ordinary Least Squares (OLS) Method is a good method to estimate regression parameters when there is no violation in classical assumptions, such as the existence of outlier. Outliers can lead to biased parameters estimator, therefore we need a method that can may not affected by the existence of outlier such as Minimum Covariance Determinant (MCD) and Least Median of Squares (LMS). However, the application of this method is less accurate when it is used for small data. To overcome this problem, it was aplicated bootstrap method in MCD and LMS to determine the comparison of bias in parameters which were produced by both methods in dealing outlier in small data. The used bootstrap method in this study was the residual bootstrap that works by resampling the residuals. By using 95% and 99% confidence level and 5%, 10%  and 15% outlier percentage, MCD-bootstrap and LMS-bootstrap give value of parameter estimators which were unbias for all percentage of outlier. We also found that the widht of range which produced by MCD-bootstrap method was shorter than LMS-bootstrap method produced. This indicates that MCD-bootstrap method was a better method than LMS-bootstrap method.
                            
                         
                     
                 
                
                            
                    
                        PERAMALAN JUMLAH KUNJUNGAN WISATAWAN MANCANEGARA KE BALI MENGGUNAKAN METODE SINGULAR SPECTRUM ANALYSIS 
                    
                    MIRA AYU NOVITA SARI; 
I WAYAN SUMARJAYA; 
MADE SUSILAWATI                    
                     E-Jurnal Matematika Vol 8 No 4 (2019) 
                    
                    Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University 
                    
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                                DOI: 10.24843/MTK.2019.v08.i04.p269                            
                                            
                    
                        
                            
                            
                                
Singular spectrum analysis (SSA) is a method to decompose the original time series into a summation of a small number of components that can be interpreted as varied trends, oscillatory, and noise components. The purpose of this research is to model and to find out the results of forecasting the number of foreign tourists arrival to Bali using SSA method. In this research, the accuracy of forecasting results will be calculated using the SSA model with reccurent singular spectrum analysis (RSSA) method. The best SSA model was obtained with a window length (L=94) and produces MAPE value of 7,65%.
                            
                         
                     
                 
                
                            
                    
                        PENERAPAN METODE PARTIAL LEAST SQUARE REGRESSION (PLSR) PADA KASUS SKIZOFRENIA 
                    
                    NI WAYAN ARI SUNDARI; 
I GUSTI AYU MADE SRINADI; 
MADE SUSILAWATI                    
                     E-Jurnal Matematika Vol 10 No 2 (2021) 
                    
                    Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University 
                    
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                                DOI: 10.24843/MTK.2021.v10.i02.p333                            
                                            
                    
                        
                            
                            
                                
Partial Least Square Regression (PLSR) is a method that combines principal component analysis and multiple linear regression, which aims to predict or analyze the dependent variable and more than one independent variable. The purpose of this study is to determine the equation model for the recurrence of schizophrenia patients using the PLSR method. The best number of components to form a PLSR model in this study is one component with a minimum RMSEP value of 0.6094 and an adjR2 value of 80.09 percent.
                            
                         
                     
                 
                
                            
                    
                        PEMODELAN ANGKA KEMATIAN BAYI DENGAN PENDEKATAN GEOGRAPHICALLY WEIGHTED POISSON REGRESSION DI PROVINSI BALI 
                    
                    M ARRIE KUNILASARI ELYNA; 
I GUSTI AYU MADE SRINADI; 
MADE SUSILAWATI                    
                     E-Jurnal Matematika Volume 1, No 1, Tahun 2012 
                    
                    Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University 
                    
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                                DOI: 10.24843/MTK.2012.v01.i01.p017                            
                                            
                    
                        
                            
                            
                                
AlphaIn this study the used method of Geographically Weighted Poisson Regression (GWPR) is a statistical method to analyze the data to account for spatial factors. GWPR is a local form of Poisson regression with respect to the location of the assumption that the data is Poisson distributed. There are factors that are used in this study is the number of health facilities and midwives, the average length of breastfeeding, the percentage of deliveries performed by non-medical assistance, and the average length of schooling a woman is married. The research results showed that factors significantly influence the number of infant deaths in sluruh districts / municipalities in Bali is the average length of schooling a woman is married. Then the results of hypothesis test obtained the results that there was no difference who significant between the regression model poisson and GWPR in Bali.
                            
                         
                     
                 
                
                            
                    
                        PEMODELAN ANGKA KEMATIAN KECELAKAAN LALU LINTAS DI KOTA DENPASAR 
                    
                    NI LUH WIWIN YUNIARTI; 
I GUSTI AYU MADE SRINADI; 
MADE SUSILAWATI                    
                     E-Jurnal Matematika Vol 10 No 2 (2021) 
                    
                    Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University 
                    
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                                DOI: 10.24843/MTK.2021.v10.i02.p329                            
                                            
                    
                        
                            
                            
                                
Denpasar City is one of the most crowded areas on the island of Bali, this is due to the fast population growth rate. This fast population can cause problems, one of the problem is in the transportation sector. The increase in the volume of transportation can cause traffic congestion which can lead to a high number of traffic accidents, this can lead to death due to traffic accidents in Denpasar City. To determine the factors that influence traffic accident mortality, researchers used Poisson regression analysis. Based on data on traffic accidents in Denpasar City in 2018, the deviance value is smaller than the chi square value. Therefore Poisson regression analysis is sufficient to model traffic accident data in Denpasar City. The Poisson regression model obtained from this research is. Based on the Poisson regression model obtained, the independent variable that contributes significantly and has a high effect on the number of people who die in traffic accidents is the driver factor.
                            
                         
                     
                 
                
                            
                    
                        ANALISIS KEKAMBUHAN ORANG DENGAN SKIZOFRENIA MENGGUNAKAN METODE PARTIAL LEAST SQUARE STRUCTURAL EQUATION MODEL 
                    
                    IRA INDRIYANTI; 
G.K. GANDHIADI; 
MADE SUSILAWATI                    
                     E-Jurnal Matematika Vol 8 No 3 (2019) 
                    
                    Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University 
                    
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                                DOI: 10.24843/MTK.2019.v08.i03.p257                            
                                            
                    
                        
                            
                            
                                
Schizophrenia is a psychotic disorder characterized by major disorders in the mind and emotions. People with schizophrenia (ODS) can experience recurrence if they do not receive proper care. The latent variable used in this study was ODS reccurence. One method that can determine the relationship between latent variables and latent variables with the indicator is the partial least square structural equation model (PLS-SEM). This study was conducted to see how the structural model of ODS recurrence data and to know the factors that most influence ODS recurrence. The results of this study concluded that the resulting model was good enough with a large R-square value of 0.8577, but not all variables used in this study had a significant effect on ODS recurrence. ODS recurrence is significantly influenced by family support and community social support variables. While medication compliance and physician control regularity will not have a significant effect without family support. The worse treatment of families and communities around ODS recurrence will occur more often.
                            
                         
                     
                 
                
                            
                    
                        KARAKTERISTIK SEKTOR PERTANIAN DI PROVINSI BALI MENURUT SUBSEKTOR PENYUSUN 
                    
                    PUTU OKA SURYA ARSANA; 
MADE SUSILAWATI; 
KETUT JAYANEGARA                    
                     E-Jurnal Matematika Vol 2 No 4 (2013) 
                    
                    Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University 
                    
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                                DOI: 10.24843/MTK.2013.v02.i04.p054                            
                                            
                    
                        
                            
                            
                                
Bali instead of famous for tourism also popular at agriculture. One of them is subak. It is a culture heritage in the world. To cope with this problem the development in agriculture should be increased. The goal for this research are to know the identifiier factors of agriculture devolopment in Bali, the most dominat factors, and the variable which represent the development of agriculture in Bali. The method of analysis used for this research is factors analysis. Factor analysis is used to reduce the data or summary, for variable which is being changed to a new variable called factor and still load many information contained in a real variable. The method used in the factor analysis is principal component analysis method. Many  factors are determined by eigen values. The  factor rotation which used is varimax rotation. Based on the research results, got seven factors with the diversities which can be explained are 76.417%. Factors dryland farming as the most dominant factor identifier with the total value of the largest eigenvalues ??is 4.564 or 25.356% with variables representing these factors are widely planted potatoes and pulses.
                            
                         
                     
                 
                
                            
                    
                        PEMODELAN JUMLAH ANAK PUTUS SEKOLAH DI PROVINSI BALI DENGAN PENDEKATAN SEMI-PARAMETRIC GEOGRAPHICALLY WEIGHTED POISSON REGRESSION 
                    
                    GUSTI AYU RATIH ASTARI; 
I GUSTI AYU MADE SRINADI; 
MADE SUSILAWATI                    
                     E-Jurnal Matematika Vol 2 No 3 (2013) 
                    
                    Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University 
                    
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                                DOI: 10.24843/MTK.2013.v02.i03.p045                            
                                            
                    
                        
                            
                            
                                
Dropout number is one of the important indicators to measure the human progress resources in education sector. This research uses the approaches of Semi-parametric Geographically Weighted Poisson Regression to get the best model and to determine the influencing factors of dropout number for primary education in Bali. The analysis results show that there are no significant differences between the Poisson regression model with GWPR and Semi-parametric GWPR. Factors which significantly influence the dropout number for primary education in Bali are the ratio of students to school, ratio of students to teachers, the number of families with the latest educational fathers is elementary or junior high school, illiteracy rates, and the average number of family members.
                            
                         
                     
                 
                
                            
                    
                        ANALISIS ANGKA KEMATIAN NEONATAL DI PROVINSI BALI DENGAN PENDEKATAN ANALISIS REGRESI 
                    
                    NI WAYAN DIAH SIHMAWATI; 
I WAYAN SUMARJAYA; 
MADE SUSILAWATI                    
                     E-Jurnal Matematika Vol 7 No 3 (2018) 
                    
                    Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University 
                    
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                                DOI: 10.24843/MTK.2018.v07.i03.p210                            
                                            
                    
                        
                            
                            
                                
Neonatal mortality rate (NMR) is the number of infant death up to 28 days expressed in 1,000 live births in the same year. The aim of this research is to obtain the best model for NMR in Bali and to find significant factors that influence NMR in Bali using multiple linear regression and spatial regression methods. The data used in this study was obtained from the Health Departement in each district in Bali.The result shows that there is no spatial dependence between regions and no interregional heterogeneity. This suggests that spatial regression is not applicable in this study. Hence, we model the NMR using multiple linear regression. Furthermore, we obtained the estimated NMR model in Bali is . In conclusion, the factors that influence the NMR are the percentage of babies with low weight and percentage of households with a clean and healthy living behavior.