Estimation of Percentage on Malnutrition Occurrences in East Java Using Geographically Weighted Regression Model

The Province of East Java has its own characteristics that differentiate it from any other regions. Dissimilarities in characteristics of a region may encompass issues such as social, economic, cultural, parenting, education, and the environment, so as to cause the difference in case of severe under nutrition between one region to another. Sufferers of malnutrition in one region may be linked and influenced by the surrounding regions. Therefore, we need a statistical modeling that is able to take into account the spatial factor. Statistical methods that can be used to analyze the data and also takes into account the spatial factor are the Geographically Weighted Regression (GWR). This study is aimed to determine the case of malnutrition models in East Java Province using GWR model with kernel adaptive bi-square weighting and comparing it to the conventional linear regression model.  The data used in the study are secondary data obtained from the National Socio-Economic Survey and Basic Health Research (2010) conducted in 38 districts in East Java. Estimation is done by using the Weighted Least Squares method that provides different weighting values to each region. The result showed that there are 38 models of the malnutrition case that is different for each district in East Java. The GWR model with bi-square kernel weighting function is better in modelling the case of malnutrition in East Java compared to the conventional linear regression models that are based on the criteria of goodness that is the R-square, Mean Square Error and the Akaike Information Criterion

Modeling of Malaria Prevalence in Indonesia with Geographically Weighted Regression

Malaria is a public health problem that can lead to death, especially in high-risk groups i.e. infants, toddlers and pregnant women. This disease is still endemic in most parts of Indonesia. The relation of location factor between regions with the surrounding region was assumed to give the effect of spatial variability in the prevalence of malaria in the region. It would lead to the prevalence of malaria modeling using classical regression methods become less precise due to the assumption of homogeneity of variance was not met. It could be overcome by Geographically Weighted Regression (GWR) modeling. In GWR analysis, the selection weighting function was one determinant of the analysis results. GWR analysis resulted on the prevalence of malaria in Indonesia, GWR model with bisquare kernel weighting function had a better value of R2 and AIC than GWR models with gaussian kernel weighting function

MODEL SPASIAL BAYES DALAM PENDUGAAN AREAKECIL DENGAN PEUBAH RESPON BINER

Model-model dalam pendugaan area kecil mengasumsikan bahwa pengaruh acak galat area saling bebas. Namun dalam beberapa kasus, asumsi ini sering dilanggar. Penyebabnya adalah keragaman suatu area dipengaruhi area sekitarnya, sehingga efek spasial dapat dimasukkan ke dalam pengaruh acak area. Rao (2003) menyatakan bahwa salah satu model dalam pendugaan area kecil yang dapat dipengaruhi oleh efek spasial adalah model Logit- Normal. Model tersebut digunakan untuk menduga proporsi melalui metode Bayes berhirarki (Hierarchical Bayes/BB). Tu.luan pertama dari penelitian ini adalah mengembangkan metode Bayes untuk data peubah respon biner dengan menambahkan efek spasial. Tujuan selanjutnya adalah membandingkan sifat-sifat statistik penduga proporsi Logit-Normal Bayes berhirarki dengan pembobot spasial tetangga terdekat (BB1) dan model Logit-Normal Bayes berhirarki tanpa pembobot spasial (BB2). Studi kasus dilakukan pada data simulasi. Hasil penelitian menunjukkan bahwa pengaruh spasial dapat memperbaiki pendugaan parameter pada area kecil yang diindikasikan dengan nilai menurunnya nilai Root mean Square Error/RNISE (29%). Bila dilihat darirata-rata persentase bias relatif (Rbias), BB1 memiliki nilai Rbias lebih kecil yaitu sebesar 33.36%o. sedangkan Rbias BB2 sebesar 44.54%. Sehingga dapat disimpulkan penduga proporsi Logit-Normal Bayes berhirarki dengan pembobot spasial tetangga terdekat lebih baik daripada penduga proporsi Logit-Normal Bayes berhirarki tanpa pembobot spasial

D-Optimal Design for Mixture Amount Experiment Involving Split-Plot Design

A mixture amount experiment (MAE) is a design that depends on the proportions of the ingredients and the total amounts. The classical MAE contains the classical mixture experiment on each total amount. Consequently, complete randomization is challenging to implement in MAE, so a split-plot design approach was proposed. In the MAE, the whole plot factor is the total amount of mixtures, while the subplot factor is the composition of the ingredients. Another problem in the MAE is if the number of ingredients and total amounts increase, the number of runs increases. The split-plot design with an optimal design approach was proposed. The study aimed to develop a point-exchange algorithm with a split-plot design approach. The case study used is a mixed design consisting of three ingredients and two total amounts of mixtures. The results obtained are that the algorithm compiled in this study produces optimal design points, namely the edge points in the design region

Pendekatan Geographically Weighted Zero Inflated Poisson Regression (Gwzipr) dengan Pembobot Fixed Bisquare Kernel pada Kasus Difteri di Indonesia

The number of deaths due to diphtheria is counts data and there is a considerable presence of zeros (excess zeros). Besides, data on the spread of disease are generally geographically oriented or observed in each particular region, which is a type of spatial data. Geographically Weighted Zero Inflated Poisson Regression (GWZIPR), as the development of Geographically Weighted Regression (GWR) and Zero Inflated Poisson (ZIP) models will be used as a model in processing provincial diphtheria data in Indonesia in 2018, with the independent variable percentage of diphtheria cases (X1), percentage of vaccinated numbers (X2) and percentage of the population (X3) in each province in Indonesia. Estimating model parameters uses the method of maximum likelihood estimation. While the weighting function used is fixed bisquare kernel. Data is processed using software R packages lctools. The results were obtained if the model involved all three independent variables, the effect of the three independent variables on the number of deaths due to diphtheria was not significant. This is because there is a strong and significant relationship between independent variables, so that if the model does not involve a variable percentage of the population (population density), the percentage of vaccinated people affects the number of deaths caused by diphtheria significantly in an area. So that the provision of immunization vaccines can reduce the number of deaths caused by diphtheri