Judul: Pemodelan Kematian yang Diakibatkan oleh Pembunuhan dan Bunuh Diri di Lithuania pada Tahun 2003-2004
Abstrak: Many daily problems can be assumed as stochastic processes. Therefore stochastic processes are very important subjects. One of them is modeling the geographical variation of mortality risk. This paper considers numerical issues of the empirical Bayesian approach model applied to the low rate estimation. The condition for nonsingularity of Bayesian estimation is given and the convenient iterative algorithm for the estimation is described. The clustering algorithm is also developed. It uses the property of Poisson-Gaussian model to treat probabilities of events in populations being the same, if the variance of probabilities is small. If the population size is not taken into account, then the estimation of relative risk (RR), which is only based on a few cases to produce a map, is not good. Empirical Bayes estimation has been proven to have smaller squared error than that of RR estimator. Integrating and maximizing likelihood functions is done by using a mathematical software. This paper refers mainly to the paper of Sakalauskas (2010) entitled On the Empirical Bayesian Approach for the Poisson-Gaussian model.
Keyword: Poisson-Gaussian model, mortality modeling, Empirical Bayesian

Judul: Pemodelan area kecil untuk menduga angka kematian bayi melalui pendekatan model regresi poisson bayes berhirarki dua-leve
Abstrak: Hierarchical Bayes (HB) approach has been recently proposed for small area estimation problems. However, in this area, there is still a limited use of hierarchical Bayesian models though they have many advantages: (i) their specification is straightforward and allows to take into account the different sources of variation and (ii) inferences are clear-cut and computationally feasible in most cases by using standard Markov Chain Monte Carlo (MCMC) techniques. Within this approach, when the variable of interest is a count or a proportion, alternative model specifications can be considered. In the HB approach, a prior distribution on the model parameters is specified and the posterior distributrion of the parameters of interest is obtained. Inferences are based on the posterior distribution, in particular, a parameter of interestis estimated by its posterior mean and its precision is measured by its posterior variance. Even though the HB is straightforward, and HB inferences are clear-cut and „exact‟, but the HB requires the specification of a subjective prior ( ) on the model parameter . Priors on maybe informative or „diffuse‟. Informative priors are based on substantial prior information, such previous studies judged relevant to the current data set y. However, informative priors are seldom available in real HB application, particularly those related to public policy. This dissertasion analyses and discusses the structure of alternative models when the variable of interest is a count, both on a theoretical side as well as by a simulation study. Bayesian specifications derived from classical models for SAE, e.g. the Fay-Harriot model (Fay and Herriot, 1979), and then consider a generalized linear Poisson model. The combination of these two models has formed a model called the hierarchical Bayes Poisson regression model. Development of HB models that extend the Fay-Herriot models and generalized linear model by using two different priors distribution on the model parameter of interest. First, we used gamma distribution as a conjugate priors for Poisson likelihood, and second we used inverse gamma distribution which is non conjugate prior for Poisson likelihood. The proposed models are implemented using the Gibbs sampling method for fully Bayesian inference. We apply the proposed models to the analysis of infant mortality rate for sub-district level in Bojonegoro district, East Java Province. Based on the application result, we found that hierarchical Bayes Poisson regression model with inverse-gamma prior distribution gave the better prediction of infant mortality rate than model with gamma prior distribution. Our conclusion were made based on various criterion on diagnostic models, such as convergence of Markov chain diagnostic tests, a Bayesian measure of fit or adequacy, residual analysis, and other summary statistics (bias and mean square error).
Keyword: small area estimation, Fay-Herriot model, generalized linear models, Poisson distribution, Markov chain Monte Carlo, Gibbs sampling.

Judul: Material Tekstil Hidrofilik Berbasis Komposit Chitosan-Pva-Zno
Abstrak: Material tekstil berbasis komposit chitosan-PVA-ZnO merupakan alternatif baru serat tekstil yang memiliki sifat hidrofilik. Tujuan penelitian ini adalah membuat dan menentukan karakteristik komposit chitosan-PVA-ZnO sebagai material serat tekstil hidrofilik. Material dibuat dengan empat formula berbeda (A, B, C, D) dengan variasi komposisi PVA-ZnO dan chitosan asetat. Berikutnya, formula komposit dibuat serat dengan teknik pemintalan basah dan film dengan pencetakan. Uji kuat tarik dan elongasi dilakukan pada serat. Adapun uji lainnya seperti uji mikroskopik, laju transmisi uap air dan sudut kontak air dilakukan pada material film. Material komposit serat yang dihasilkan memiliki diameter rata-rata 1,52 ± 0,13 mm dan panjang 201,25 ± 21,34 mm. Secara mikrostruktur material berbentuk pori-pori, bersifat higroskopis, dan permukaan yang diselimuti bintil putih. Kuat tarik yang dihasilkan 21,70 ± 0,84 N hingga 27,63 ± 0,01 N, elongasi 321,51 ± 1,41 % hingga 352,97 ± 1,36 %, laju transmisi uap air 0,545 ± 0,120 g/m2/24 jam hingga 3,620 ± 1,188 g/m2/24 jam dan sudut kontak air 45,0 ± 0,0° hingga 55,0 ± 1,0°, yang menunjukkan komposit memiliki sifat hidrofilik.
Keyword: chitosan, hidrofilik, komposit, serat tekstil