Judul: Model bayes untuk pendugaan area kecil dengan penarikan contoh berpeluang tidak sama pada kasus respon binomial dan multinomial
Abstrak: In this research a Bayesian Method of Small Area Estimation (SAE) has been developed based on binomial and multinomial response variables using Susenas data obtained from unequal probability sampling. Case study was carried out to predict education level of the population measured by literacy rate and mean years of schooling in sub-district level in East Java Province. The SAE model for binomial response was developed with two methods, i.e. using weighted logit normal mixed model and involving the probability of sampling selection model as exponential function into the SAE model. A simulation study was carried out by implementing 100 times sampling selection into population data. Penalized Quasi Likelihood (PQL) and Restricted Maximum Likelihood method (REML) was used to parameter estimation of SAE model. Based on the simulation result, we found that the weighted logit normal mixed model gave the best estimate. In application, the weighted logit normal mixed model also provided good prediction of literacy rate in Sumenep and Pasuruan regency.For the multinomial respons, we applied the weighted logit multinomial mixed model. MSE estimation was used Jackknife method and it gave very small MSE of about 1,14 x10-7.
Keyword: 

Judul: Metode Kemungkinan Berhirarkhi untuk Pendugaan Area Kecil dengan Respon Beta-Binomial dan Peubah Penyerta yang Mengandung Kesalahan Pengukuran, Metode Kemungkinan Berhirarkhi untuk Pendugaan Area Kecil dengan Respon Beta-Binomial dan Peubah Penyerta yang Mengandung Kesalahan Pengukuran
Abstrak: This research is motivated by developing a model on small area estimation. Small Area Estimation (SAE) is a statistical method to estimate parameters in a subpopulation with a small number of samples or none. The SAE model can be applied to data with continuous and discrete interest variables. Research on binary data is prone to Overdispersion. According to Wagner et al., Overdispersion can be overcome using a mixed distribution of Beta-Binomial. Rao and Molina developed a Beta-Binomial model based on SAE by estimating the proposed parameters using the Bayesian approach. According to Lee and Nelder, estimating the parameters of the mixed model with the Beta-Binomial hierarchy can be done through Hierarchical Likelihood (HL) and Adjusted Profile Hierarchical Likelihood (APHL). This method is claimed to be better than the Bayes approach analytically. In addition, the properties of small area estimators, such as bias and MSE, are conditionally derived from the additional information assuming that the auxiliary variables are measured without error. However, when the auxiliary variables used in the model are measured by error, the small area estimator that ignores the error may be worse than the direct estimator. In the regression model, measurement error in the auxiliary variables is known to bias the estimation of model parameters and lose the power to detect interesting relationships between variables. In a small area model, using survey data for the auxiliary variables means that the data can be measured with error. The researchers are interested in developing a Beta-Binomial model for a small area estimation without and with measurement errors on the auxiliary variables. Parameter estimation be done through HL and APHL. The proposed models are called SAE-BB-HL, SAE-BB-APHL, and SAE-ME-BB-HL. Furthermore, the proposed models are evaluated and investigated through a simulation study. The empirical study used illiteracy rate data from Bengkulu Province and East Java Province in 2021. The simulation results of fixed effect, dispersion, and overdispersion estimators of SAE-BB-HL are indicated to be biased. However, in the majority, the MSE value of the HL estimator was lower than that of the direct estimator of MSE. From this value and the bias value of the HL estimator, which is smaller than the direct estimator, it can be said that the SAE-BB-HL model can improve the precision of proportion estimation. This study also carried out the proportion estimation in a small area. The estimator of the proportion (p ̂_i^HL) from several areas has the same tendency. In other words, the SAE-BB-HL model has good flexibility. The difference between the SAE-BB-APHL and SAE-BB-HL models lies in the estimation of the fixed effect parameter β_z in the proposed model is done through the maximization of the APHL function. MSEP values were analyzed using analysis of variance (ANOVA) based on a complete factorial design with five factors. The results showed that all the main factors had a significant effect on the MSEP score. Moreover, the highest interaction was statistically significant. Tukey's HSD analysis was then performed to analyze the different influences of each significant interaction factor. There were 48 treatments tested. The SAE-BB-APHL model with area size m
Keyword: Binary Response, Hierarchical Likelihood, Measurement Error, MSE, Small Sample

Judul: Hydroxyapatite-Chitosan Composite Coating on Stainless Steel 316 to Improve Corrosion Resistance
Abstrak: Metals that were used in bone implantation should have good corrosion resistance that were not easily corroded when implanted into the body. Metal corrosion resistance of stainless steel 316 can be enhanced through the metal coating by hydroxyapatite-chitosan composite with composition ratio of hydroxyapatite (gram) and chitosan 3% (mL) 0.2:0, 0.2:0.1, 0.2:0.5, 0.2:1.0, and 0.2:1.5. Besides improving the corrosion resistance of metal, the presence of chitosan also maintain the position of hydroxyapatite on metal surfaces. Corrosion resistance increases with increasing amount of chitosan added and the best composition of hydroxyapatite-chitosan composite was 0.2:1.0.
Keyword: