Judul: Kajian bias metode area-specific jackknife dan bias metode weighted jackknife dalam pendugaan area kecil untuk respon poisson dengan pendekatan bayes
Abstrak: Small area estimation is a method to estimate parameters in a subpopulation with small sample size. The method is based on indirect estimation using the strength of the surrounding area and data sources outside the area to obtain statistic with edequate precision. Empirical Bayes (EB) method can be used to obtain estimates of small area parameters. In this paper the method was used to handle count data responses. The quality of an estimate can be measured by mean squared error (MSE). Wan (1999) proposed a weighted jackknife method for finding MSE of EB and showed that weighted jackknife methods have desirable asymptotic properties. The concept of this method is to put weights using hat matrix of auxiliary variables. Rao (2003) proposed a modification of jackknife method described in Jiang et al. (2002). This method leads to a computationally simpler jackknife estimator with an area-specific leading term. In the simulation study for Poisson responses, the relative bias of the area-specific jackknife estimator has been shown as the best MSE estimator in small number of areas for Poisson-Gamma model. For Poisson-Lognormal model, the relative bias of the weighted jackknife estimator has been shown as the best MSE estimator. Finnaly, this method was applied to estimate small area mean squared errors in disease mapping problems.
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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: Kadar Delstrosa, Levulosa, Maltosa, serta Sukrosa Madu Segar dan Madu Bubuk dengan Bahan Pengisi Campuran Gum Arab dan Delstrin
Abstrak: Madu iilerupakan sumber karbohidrat yang komponen utamanya adalah gula berbentuk monosakarida yaitu levulosa dan dekstrosa. Madu bersifat higroskopis yaitu inenyerap air sehingga dapat menyebabkan kerusakan pada madu berupa fermentasi. Salah satu cara mengawetkan madu adalah pengeringan karena pengeringan dapat menghilangltan sebagian besar air dari madu. Pengeringan juga dapat mempermudah penanganan madu seperti pengemasan, penyimpanan maupun pengangkutannya.
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