Judul: Determining Optimum Strata Boundary Points on Stratified Random Sampling using Dynamic Programming (Case: Expenditure per Capita of East Java Province Year 2008)
Abstrak: Optimum stratification is the method of choosing the best boundaries that make strata internally homogeneous, given some sample allocation. In order to make the strata internally homogenous, the strata should be constructed in such a way that the strata variances for the characteristic under study be as small as possible. This could be achieved effectively by having the distribution of the main study variable known and create strata by cutting the range of the distribution at suitable points. The problem of finding Optimum Strata Boundaries (OSB) is considered as the problem of determining Optimum Strata Widths (OSW). The problem is formulated as a Mathematical Programming Problem (MPP), which minimizes the variance of the estimated population parameter under Neyman allocation subject to the restriction that sum of the widths of all the strata is equal to the total range of the distribution. The distributions of the study variable are considered as continuous with standard normal density functions. The formulated MPPs, which turn out to be multistage decision problems, can then be solved using dynamic programming technique proposed by Bühler and Deutler (1975). After the counting process using C++ program received the width of each stratum. From these results the optimal boundary point can be determined for each stratum. For the two strata to get the optimal point on the boundary x1 = 0.002. For the formation of three strata obtained the optimal point on the boundary x1 = -0.546 and x2 = 0.552. For the formation of four strata obtained optimal boundary point is x1 = -0.869, x2 = 0.003 and x3 = 0.878. In forming five strata obtained optimal boundary point x1 = -1.096, x2 = -0.331, x3 = 0.339 and x4 = 1.107. The establishment of a total of six strata obtained the optimal point on the boundary x1 = -1.267, x2 = -0.569, x3 = 0.005, x4 = 0.579 and x5 = 1.281.
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Judul: Study on the Effects of Cluster Information in Prediction of Non-sampled Area (A Case Study of per Capita Expenditures at Subdistrict Level in Regency and Municipality of Bogor).
Abstrak: Empirical Best Linear Unbiased Predictor (EBLUP) has been widely used to predict parameters in area with small or even zero sample size, known as non-sampled area. It has been noted that there is a problem when this model will be used to predict the parameters of non-sampled area. Usually EBLUP is used to predict the parameters using a synthetic model ignoring the area random effects due to lack of non-sampled area information. Hence, this prediction will be distorted based on a single line of the synthetic model. The idea developed in this thesis is to modify the prediction model by adding cluster information assuming that there are similiarities among particular areas. These information have been incorporated into the model to modify the intercept of prediction models as well as both intercept and slope of the prediction model. In this paper, a simulation is carried out to study the performance of the proposed models compared with ordinary EBLUP. All models were evaluated based on the value of Relative Bias (RB) and Relative Root Mean Squares Error (RRMSE). It was shown, by mean of simulation, that the addition of cluster information has improved the ability of the model to predict non-sampled areas. Restricted Maximum Likelihood (REML), a common method for estimating variance component in EBLUP models, requires normality assumption. But the conditions in which the area random effects or sampling error are not normally distributed may encountered in many applications. Therefore we also used different scenarios, such as either one of random component was not normally distributed or both of area random effects and sampling error area were not normally distributed, to study the performance of the proposed models when the area random effects or auxiliary variables are not normally distributed. The result showed that under these conditions, the proposed models has been able to estimate the parameter with smaller Relative Bias (RB) and Relative Root Mean Squares Error (RRMSE) than ordinary EBLUP, especially in non-sampled areas. It was shown that all models could be used to predict average per capita expenditures per month at subdistrict level in regency and municipality in Bogor. The analysis was based on SUSENAS 2010 and PODES 2011 data sets. Even though the resulting predictions of the models were different, similar pattern among them has been observed. Clustering technique played an important role in implementing the proposed model in the case study. Clustering pattern which tend not to be linearly correlated with response variable can lead to the result that proposed model was not better than standard EBLUP model. However, there were some proposed models that showed a better accuracy than the standard EBLUP prediction of non-sampled subdistrict parameter.
Keyword: EBLUP, Clustering, Linear Mixed Models

Judul: Karakterisasi molekuler virus gemini asal tanaman tomat
Abstrak: Virus gemini merupakan salah satu kelompok patogen utama yang menyerang dan menimbulkan kerusakan yang besar pada tanaman sayuran di daerah tropik dan sub tropik. Pada umumnya kerugian yang ditimbulkan oleh adanya suatu epidemi berkisar antara 30%-100%. Gejala tanaman tomat yang terinfeksi oleh virus gemini kadangkala mirip dengan gejala yang disebabkan oleh infeksi virus yang lain khususnya Potyviridae dan Tobamoviridae. Dengan demikian, gejala penyakit saja tidak cukup untuk digunakan dalam mengidentifikasi virus gemini. Pengetahuan tentang karakter molekuler virus gemini pada tanaman tomat yang bergejala menjadi sangat penting dalam mengidentifikasi virus gemini tersebut. Penelitian ini bertujuan untuk mengamati gejala luar, melakukan purifikasi virus dan mempelajari beberapa karakter molekuler virus gemini asal tanaman tomat. Karakter molekuler yang dipelajari meliputi ukuran dan jenis asam nukleat serta ukuran protein. Deteksi virus dengan metode polymerase chain reaction (PCR) dilakukan untuk memperkuat hasil analisis karakter molekuler. Isolat virus gemini asal tanaman tomat yang diperoleh dari daerah Bandung, Jawa Barat ditularkan dengan serangga vektor B. tabaci dari dan ke tanaman tomat. Gejala yang ditimbulkannya berupa penebalan tulang daun permukaan bawah, daun menggulung ke atas atau ke bawah, berkeriput dan tepi daun mengalami klorosis. Virus gemini isolat Bandung ini berhasil dimurnikan dari daun tomat yang bergejala dengan menggunakan metode yang digunakan ..dst
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