Penggunaan Regresi Kuantil Multivariat pada Perubahan Trombosit Pasien Demam Berdarah Dengue
Widya Nauli Amalia Puteri, Anna Islamiyati, Anisa Anisa
Quantile regression is an extension of the regression model of conditional quantile where the distribution is derived from the response variable expressed as a co-variate function. Quantile regression can model data that contain outliers. Patterns of platelet change in DHF patients based on body temperature and white blood cells were analyzed by quantile regression using θ = 0,25; 0,50, and 0,75. Based on the parameter estimation results, the quantile θ = 0,25 and 0,75 obtained variables that affect the platelets of DHF patients are white blood cells. Significant differences from the variables in each quantile occur because of the possibility of other factors that influence the platelets of DHF patients that are not contained in the model. The difference in the influence of factors on each quantile requires an appropriate adjustment of medical measures so that efficiency can be obtained in handling DHF patients.
This research aims to describe the parameter estimation of the regression model on the panel data by approaches of Fixed Effects Model with within a group method. This research aims to determine the factors that influence the Human Development Index in South Sulawesi Province in 2011-2017 using Panel Data Regression Analysis. The regression model was obtained from the maximum likelihood estimation using within group approach using a mean for each independent variable and the dependent variable to find out the intercept differences in each city or cross-section that explains the effect of regional differences and to find out the intercept differences for cross sectional or time series. The results showed that the average length of the school variable (𝑋1) and life expectancy variable (X2) significantly affects the Human Development Index (Y) in the Province of South Sulawesi in 2011-2017.
Bayesian Conditional Autoregressive (CAR) dengan Model Localised dalam Menaksir Risiko Relatif DBD di Kota Makassar
Rusydah Khaerati, Andi Kresna Jaya
Bayesian Conditional Autoregressive (CAR) is used in disease mapping because it is able to model relative risks by taking into account the smoothing of the estimated relative risk and entering spatial information to reduce the errors of the estimated relative risk parameters so that a more reliable relative risk model is obtained. In this study, the relative risk value of the spread of dengue fever will be calculated using Bayesian CAR with the localised model. These results were obtained using the OpenBUGS program and are illustrated in the 2016 dengue fever case data. Based on the model, mapping of dengue fever in Makassar can be identified in each district and shows that Makassar City is still very vulnerable to dengue fever.
Hubungan Faktor Kolestrol Terhadap Gula Darah Diabetes dengan Spline Kubik Terbobot
Zhazha Alifkhamulki Ramdhani, Anna Islamiyati, Raupong Raupong
Diabetes Mellitus (DM) is often recognized through an increase in a person's blood sugar level. Factors that can affect the increase in blood sugar levels of DM patients one of which is cholesterol. It usually contains the bookkeeping of several types of cholesterol, including LDL and total cholesterol. DM data are assumed to experience heterokedasticity so that in this study analyzed using regression of weighted cubic spline nonparametric. The estimation method used is weighted least square (WLS). This study aims to obtain a weighted cubic spline model on cholesterol based DM data. The selection of the best model can be seen based on the criteria for the value of generalized cross validation (GCV) minimum. Based on the analysis obtained weighted cubic spline models for cholesterol factors for blood sugar as follows:
Kemampuan Estimator Spline Linear dalam Analisis Komponen Utama
Samsul Arifin, Anna Islamiyati, Raupong Raupong
In the formation of a regression model there is a possibility of a relationship between one predictor variable with other predictor variables known as multicollinearity. In the parametric approach, multicollinearity can be overcome by the principal component analysis method. Principal component analysis (PCA) is a multivariate analysis that transforms the originating variables that are correlated into new variables that are not correlated by reducing a number of these variables so that they have smaller dimensions but can account for most of the diversity of the original variables. In some research data that do not form parametric patterns also allows the occurrence of multicollinearity on the predictor variables. This study examines the ability of spline estimators in the analysis of the main components. The data contained multicollinearity and was applied to diabetes mellitus data by taking cholesterol type factors as predictors. Based on the estimation results, one main component is obtained to explain the diversity of variables in diabetes data with the best linear spline model at one knot point.
Estimasi Parameter Structural Equation Modeling Terhadap Kepuasan Pelanggan Layanan Telekomunikasi Menggunakan Metode Maximum Likelihood
Dwicahyo Ramadhan Priyatna, Raupong Raupong, La Podje Talangko
Structural Equation Modeling is a statistical technique that is able to analyze the pattern of simultan linear relationships between indicator variables and latent variables. In this study using structural equation modeling to analyze the relationship between perceived quality, perceived value, perceived bestscore, and customer satisfaction. The purpose of this study is to obtain the result parameter model estimation of structural equation modeling using maximum likelihood method and to obtain the level of students satisfaction from faculty of Mathematics and Natural Science Hasanuddin University toward Tri operator. Data collected by distributing questionnaire. Collecting sample in this study using Proporsional Random Sampling technique. To measure the level of students satisfaction from faculty of Mathematics and Natural Science Hasanuddin University toward Tri operator, the model chosen is the model used to measure Indonesian Customer Satisfaction Indeks. From the result of this study obtained in the amount of 92,04% with very satisfied criteria level of students satisfaction from faculty of Mathematics and Natural Science Hasanuddin University toward Tri operator with very satisfied criteria.
