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: Analisis Spasial untuk Data Pekerja Anak di Pulau Jawa dan Faktor-faktor yang Mempengaruhinya
Abstrak: The population of Indonesia were 237.6 million people and about 82 million of whom are children population (SP,2010). Child is a person under 18 years old, which have the potential to be developed in order to active role in the development of the nation in the future. In Law No.23 of 2002, mentioned rights acquired children population, but in the field of employment are 3.4 million children aged 10-17 years old who worked (BPS 2012). Child labor is a social problem for a country, because it will have a negative impact on the quality of a country's future successor. Three contributor to the province's largest number of child workers in the provinces of West Java, Center Java and the East Java, suspected in a region close relationship problem of child labor. Thus this study aims to see the relationship closeness in the area of child labor problems in Java, which saw the city/regency where become a hotspot and coldspot area, and analyzes the factors that influence it. The spatial correlation in this study seen based on the value of the moran indexs, and to see the visually of spatial correlation is based on the moran scatter plot. Variables that are used in the preparation of the model are variables that are not multikolinear. The child labor data is a count and have a poisson distibution, so model of Poisson Regression Analysis is a model that is used to view the amount of the alleged value of the parameter of each explanatory variable. Breusch Pagan Test (BP-Test) was conducted to determine the presence of spatial diversity. BP-Test results indicate the existence of spatial diversity means Poisson model of analysis results can not be used in general for all cities/regencies in Java, therefore the local model analysis used Poisson Geographically Weighted Regression (GWPR). The results showed the model Poisson Regression Analysis results formed AIC has a value of 385293, while the local model GWPR AIC has a value of 207010, it means that the best model is the local model GWPR the child labor problem in Java. GWPR local models also has the assumptions of nonstationary, it is seen by comparing the standard error Poisson Regression Analysis models and the Inter Quartile Range (IQR) of GWPR. Seven variables used in the preparation of the Poisson regression analysis models and local models GWPR already represent all aspects (child, family and common ). The child aspects explained by the variable numbers of children do not attend school, primary school gross enrollment rate, gross enrollment rate of SMP, and children who are married. Family aspect is represented by the variable average monthly household expenditure (expand), while the common aspect is represented by variable illiterate and Total of SMA.
Keyword: Poisson Regression Analysis, Child Labor, Geographically Weighted Poisson Regression (GWPR), Java Island

Judul: Sintesis 1-Kloro-2-metoksi-6-propilnaftalena-1,3-dioksolana (1:1) sebagai Prekursor Asam (S)-(+)-(6-Metoksi-2-naftil)propanoat
Abstrak: Senyawa 1-kloro-2-metoksi-6-propilnaftalena-1,3-dioksolana (1:1) sebagai prekursor asam (S)-(+)-(6-metoksi-2-naftil)propanoat disintesis dari bahan awal 2-metoksinaftalena. Tahapan sintesis diawali dengan klorinasi menggunakan 2 jenis pereaksi klorinasi, yakni isosianurat terklorinasi dan NaOCl. Klorinasi dengan isosianurat terklorinasi berhasil menyintesis 1-kloro-2-metoksinaftalena sebagai produk monoklorinasi melalui pencirian dengan kromatograf gas-spektrometer massa (GC-MS). Nisbah konversi produk paling efisien diperoleh pada laju penambahan 2.5 mmol/jam. Sebaliknya, klorinasi dengan NaOCl menghasilkan produk triklorinasi. 1-Kloro-2-metoksinaftalena selanjutnya diasilasi dengan propanoil klorida, tetapi analisis dengan GC menunjukkan bahwa produk asilasi tidak terbentuk. Sistem bebas-air diperlukan untuk keberhasilan proses asilasi tersebut.
Keyword: isosianurat terklorinasi, NaOCl, nisbah konversi