Judul: Model campuran linier terampat untuk respon kemiskinan ordinal dalam area tersarang
Abstrak: The Linear Mixed Models in this study is a development of Spatial Generalized Linear Mixed Model proposed by Zhang and Lin (2008). As in Zhang’s and Lin’s model, spatial (regional) data in this study is concerned on the hotspot detection. Hotspot detection method used by Zhang and Lin was Circle Based Scan Statistic (SS) method of Kulldorf (1997), while research in this dissertation using Upper Level Set Scan Statistic (ULS) hotspot detection method of Patil and Taillie (2004). Application of this hotspot detection method begins by comparing the two methods through simulation to obtain 14 performance criteria, resulting that the ULS hotspot detection method is better than the other one. Furthermore, the ULS method is performed to detect hotspot of bad nutrition in some districts, the results are used as a covariate in the modeling. This study focuses on the development of models for regional data viewed from the proximity of nested observations. According to Cressie (1993) there is a tendency for adjacent observations have a stronger correlation than distant observations. In statistics, also could be said there are differences in the variation of individuals within a group with individuals between groups. This condition must be considered in the modeling. Generalized estimating equation (GEE) is a parameter estimation method accounts for the correlation between observations. Working correlation matrices (WCM) is an important part in the parameters estimation process. Three structures of correlation matrices are studied and implemented to know which structure is the most appropriate to the data. The results of parameters estimation of Nested GLM and Nested GLMM based on combinations of some WCMs and parameter estimation techniques were compared. Response variable used in the model is in ordinal scale having complexity in the modeling, which also a focus of this research, while response variable used in Zhang’s and Lin’s model is a count variable with Poisson distribution. This ordinal response is obtained by grouping the ranking result by ORDIT (Ordering Dually in Triangles) ranking method from Myer and Patil (2010). Through the development of the model in this study involving nested spatial data, better results is provided especially when using diagonal working correlation matrix.
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Judul: Modified local getis statistic on AMOEBA weights matrix for spatial panel model and its performance
Abstrak: Classical regression model assumes that no interaction between objects. However, in some cases these conditions are not hold so we have to find an alternative solution that can accommodate the interaction effect. Spatial model is a model that can accommodate the interaction effect by adding a spatial component in the right hand side that represented by a spatial weights matrix. Generally, spatial weighted matrix uses closeness concept among the units, without include the proximity of interest variables. Another way to construct a spatial weights matrix is to use AMOEBA procedure introduced by Aldstadt and Getis. In this procedure, each element of the matrix, beside is determined by the closeness relationship, also determined by proximity among variables using local Getis statistics. Getis and Ord have claimed that the statistic is normally distributed. However, empirically, normal curve test shows that normality of local Getis statistic is influenced by variable of interest (original variable). Due to this reason, we propose a modification of local Getis statistic, namely Gnew, which gives a robust result. A modification of local Getis statistics by transforming to ( ) gives a robust result, that is, it has normal distribution for any distribution of Xj. To assess local Getis and modified local Getis statistics, they are applied on AMOEBA procedure to create weights matrix, namely WG and WGnew. We compare them with contiguity matrix (WC). The model that used for comparison is spatial dynamic panel model with respect to Cizek. By using simulated data and root mean square error relative criteria, it is found that WGnew gives the best performance. In the next stage, we evaluate spatial matrix performance in model to real data, whereas in this case, we take poverty issue in Center Java Province. Poverty is one of the issues of concern at both the central and local government. Central Java was the province with the second largest number of poor people in Indonesia. There are many factors that influence poverty, some of them are GDP per capita, population, education, share of agriculture labor, share of industry labor, share of trading labor and share of services labor (four dominant sectors). Focus of this research is to study influence of labor of junior high school graduates, population, GDP per capita and the share of labor in four dominant sectors to the number of poor people using spatial panel model. Sources of data for modeling obtained from central BPS and Center Java BPS from 2008 to 2012. The research results show that an increase in population, labor of junior high school graduates and share of agricultural labor can increase the number of poor people. Meanwhile the increase in labor share of industry, trade and service sectors and GDP per capita can decrease the number of poor people. Based on these findings, there are several ways to reduce the number of poor people, such as to control population by family planning program (KB program), improving quality of human resources, expand some business, especially in sectors of industry, trading and services.
Keyword: poverty, PDB per capita, random permutation, local Getis statistic, modified local Getis statistic

Judul: Definisi Kalsium Dan Fosfor Pada Sapi
Abstrak: Kalsium dan fosfor mempunyai peranan penting dalam beberapa proses faali tubuh. Fungsi utama kalsium adalah mengatur iri tabilitas neuromuskuler, kontraksi otot, pembekuan darah, kofaktor beberapa sistim enzim dan sebagai komponen penting dalam pembentukan tulang (Rechcigl, 1978). Fosfor adalah komponen utama ikatan energi tinggi yaitu ATP (Adenosin Tri Phosphate), merupakan komponen nukleoprotein, mengatur pH isi rumen dan sebagai sumber multiplikasi flora dan fauna rumen (Payne, 1977).
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