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: Model Zero Inflated Binomial pada Pendugaan Area Kecil dengan Aplikasi pada Data Kematian Bayi di Indonesia.
Abstrak: Kematian bayi merupakan kejadian biner, yaitu mati atau hidup di tahun tertentu yang disebut respon biner dengan peluang sukses (mati), 𝜋�������, dan batas atas (𝑛�������). Data seperti ini biasanya mengikuti sebaran binomial (𝑛�������, 𝜋�������). Ketika penarikan contoh dalam studi kematian bayi pada setiap desa di Provinsi Jawa Barat, Indonesia, terjadi banyak nilai nol pada jumlah kematian bayi di banyak wilayah/desa, akibatnya teknik statistika standar seperti regresi dan GLM tidak berlaku. Oleh karena itu, model zero inflated harus digunakan. Model ini disebut model dua-komponen. Dalam statistik, model zero inflated adalah model statistika yang berdasarkan sebaran peluang zero inflated, yaitu sebaran yang memperhitungan peluang terjadinya amatan bernilai nol. Seringkali, banyaknya nilai nol dalam contoh tidak dapat diakomodasi dengan baik oleh model binomial. Oleh karena itu, model baru diperlukan untuk menangani jenis ini data, seperti zero inflated Poisson (ZIP), zero inflated negative binomial (ZINB), model Hurdle, dll. Penelitian ini mengusulkan model zero inflated binomial (ZIB) untuk mengatasi overdispersi/kelebihan nilai nol dalam sebaran binomial. Model ZIB bekerja dengan dua komponen yang sesuai dengan proses pembangkitan dua komponen nilai nol, yaitu sebaran biner yang menghasilkan kelebihan nilai nol (model logit umum digunakan) dan sebaran binomial yang menghasilkan nilai cacahan. Metode pendugaan area kecil (SAE) yang biasa menggunakan model linier dan mengasumsikan peubah respon serta pengaruh acak area menyebar normal. Pada data yang tidak menyebar normal, metode SAE biasa dapat menghasilkan bias dan penduga yang tidak stabil. Penelitian ini mendiskusikan studi metode SAE untuk mengatasi data binomial yang mengalami overdispersi. Pada jenis data tersebut, model binomial kurang pas dan kesalahan jenis I dapat meningkat atau higher false positive rates. Penelitian ini mengusulkan model zero inflated binomial (ZIB) dalam hal pendugaan area kecil kemudian menerapkan model ZIB untuk menduga proporsi kematian bayi di Indonesia. Pendekatan dasar untuk model tersebut berdasarkan dua-komponen model dengan peluang campuran sebesar 𝑝�������. Pertama, model linier campuran terampat untuk bagian nilai bukan nol dari sebaran binomial (𝑛�������, 𝜋�������) yang diasumsikan. Kedua, model linier campuran terampat untuk indikator biner pada nilai nol atau bukan. Penelitian ini menggunakan penduga terbaik empirik logit (logit empirical best predictor, EBP) untuk menghasilkan dugaan dari model ZIB pada SAE, yang kemudian akan diterapkan pada data kematian bayi di Indonesia. Kebaikan model diinvetigasi secara analitik menggunakan simulasi dan bootstrap. Hasilnya menunjukkan bahwa metode yang diusulkan lebih baik dibanding model binomial pada SAE. Metode tersebut dapat digunakan untuk menduga proporsi kematian bayi di setiap area sebaik di setiap kota/kabupaten di Jawa Barat, Indonesia.
Keyword: bootstrap, prediksi terbaik empirik, kematian bayi, pendugaan area kecil, zero inflated binomial

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