Judul: Geographically Weighted Poisson Regression (GWPR) for Analyzing The Malnutrition Data (Case Study: Java Island in 2008
Abstrak: Poisson regression, namely global model is a statistical method used to analyze the relationship between the dependent variable and the explanatory variables, where the dependent variable is a counted data and has a Poisson distribution. The result of its parameter estimation is homogeneous for all of the observations. However, especially in spatial data, its estimation will produce biased estimation. The parameter estimates in each location will vary among regions as it is influenced by territorial or geographical factors, which is known as spatial variability or spatial non-stationarity. Therefore the appropriate analysis for this data is Geographically Weighted Poisson Regression (GWPR) model. GWPR parameter estimation used a weighting matrix which depends on the proximity between the locations. Fisher scoring iteration is used for solving the iteratively parameter estimation. In this research, GWPR will be used in malnutrition data because malnutrition is counted data which is assumed to have a Poisson distribution and the indirect factors of differences in the number of malnourished patients in every region is possible due to spatial factors. The results showed that GWPR model has better performance than global model based on AICc difference. Poverty aspect was the most influencing factor to the number of malnourished patients in a region compared to health, education, and food aspect. The spatial variability map is created for eight variables used in selected global model where every map showed the variability of local parameter estimates. There were five groups of the local parameter estimates in each map based on percentiles grouping which showed the low until hight relation of the parameter estimates groups to the number of malnourished patients. This research also created a significant variables map which detects the variables that were significant in each region.
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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