Judul: Pemodelan Area Kecil Untuk Menduga Angka Kematian Bayi Melalui Pendekatan Model Regresi Poisson Bayes Berhirarki Dua-Level Pembimbing
Abstrak: Pembahasan mengenai pendekatan model Bayes berhirarki yang diterapkan pada pendugaan area kecil untuk data yang berbentuk cacahan relatif masih sedikit. Penggunaan pendekatan hirarki Bayes yang saat ini masih terbatas, padahal pendekatan ini mempunyai beberapa keuntungan, diantaranya yaitu: (1) spesifikasi modelnya adalah langsung dan dapat digunakan untuk memodelkan berbagai sumber keragaman yang berbeda, (2) masalah inferensinya relatif lebih jelas dan komputasinya juga relatif lebih mudah dengan menggunakan teknik rantai Markov Monte Carlo. Dalam pendekatan Bayes berhirarki, dinyatakan terlebih dahulu sebaran prior pada parameter model, kemudian diperoleh sebaran posterior dari parameter yang diamati. Secara khusus, inferensi yang didasarkan pada sebaran posterior untuk parameter tersebut diduga melalui rata-rata posterior, dan presisinya diukur melalui ragam posterior. Walaupun pendekatan Bayes berhirarki ini mempunyai beberapa kelebihan, namun pendekatan ini memerlukan spesifikasi mengenai subjektif prior, ( ), pada parameter model . Prior pada parameter bisa jadi merupakan prior yang bersifat informatif. Prior yang informatif yang berdasarkan pada informasi prior menurut hasil kajian penelitian sebelumnya yang relevan dengan gugus data yang sedang dikaji. Namun demikian, dalam prakteknya prior informatif ini jarang tersedia, terutama apabila diterapkan pada masalah kebijakan publik. Dalam disertasi ini menganalisis dan membahas tentang struktur model alternatif pada saat peubah respons yang diamati berbentuk data cacahan, baik secara teoritis maupun melalui studi simulasi. Spesifikasi Bayes diturunkan melalui model Fay-Harriot (Fay dan Harriot, 1979) dan model Poisson linear terampat. Kombinasi dari kedua model tersebut menghasilkan suatu model yang disebut dengan model regresi Poisson Bayes berhirarki. Pengembangan model Bayes berhirarki yang memadukan konsep model Fay-Harriot dan model linear menggunakan dua buah sebaran prior yang berbeda, yaitu sebaran prior gamma yang merupakan prior yang bersifat conjugate bagi likelihood Poisson, serta invers-gamma yang merupakan prior yang bersifat non-conjugate bagi likelihood Poisson. Model yang diusulkan dalam disertasi ini diimplementasikan dengan menggunakan percontohan Gibbs. Model ini kemudian diaplikasikan untuk menganalisis angka kematian bayi level kecamatan di Kabupaten Bojonegoro, Provinsi Jawa Timur. Berdasarkan hasil aplikasi ini ditemukan bahwa model regresi Poisson Bayes berhirarki dengan sebaran prior invers-gamma memberikan prediksi yang lebih baik dibandingkan dengan model dengan sebaran prior gamma. Kesimpulan ini dibuat berdasarkan pada berbagai kriteria pada diagnostik model, seperti uji diagnostik konvergensi rantai Markov, ukuran kecocokan model Bayes, analisis residu, serta beberapa ringkasan statistik lainnya (bias dan kuadrat tengah galat).
Keyword: small area estimation, Fay-Herriot model, generalized linear models, Poisson distribution, Markov chain Monte Carlo, Gibbs sampling.

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: Elektrodeposisi Film Tipis Semikonduktor Cu2ZnSnS4 (CZTS) pada Substrat Kaca ITO
Abstrak: Semiconductor Cu2ZnSnS4 (CZTS) thin film that can be used as absorber layers on solar cell module was made via electrodeposition method. CZTS thin film was electrodeposited on the surface of a silicate glass coated with a thin layer of indium tin oxide (ITO). Deposition process was done using potentiostatic mode at room temperature with a potential of 1.05 V for 30, 45, and 60 minutes, followed with annealing the film at 2 different temperatures, 500 ºC using furnace and 180 ºC using hot plate, both treatments for 45 minutes in N2/H2S atmosphere. The X-ray difractograms were analyzed using Match 2 program and showed that the CZTS thin films had kesterit crystalline structure, with a low percentage of CZTS phase. The thin film also contained secondary phases such as Cu2S, ZnS, and SnS. The obtained CZTS thin films had a thickness between 1.039 and 1.676 μm. Absorption study showed that the band gap energy of CZTS thin films which was annealed using furnace ranged from 1.50 to 1.52 eV, whereas that annealed using the hot plate was not observed. The band gap energy value showed that CZTS thin films can be used as sunlight absorber layers on solar cell. The formation of CZTS crystal film on this investigation was effectively performed using furnace annealing process at a temperature of 500 ºC with deposition time of 45 minutes.
Keyword: Thin film, Electrodeposition, Cu2ZnSnS4