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: Spatial Autoregressive Poisson Model for Detecting Influential Factors on the Number of patients with Malnutrition in the Province of East Java
Abstrak: One indicator of poverty can be seen from the large of people with malnutrition. Genesis residents suffer from malnutrition in the Province of East Java is a rare occurrence, so the incidence of people suffering from malnutrition can be assumed to follow a Poisson distribution. Although the incidence of people suffering from malnutrition including a rare occurrence, but malnutrition is a serious problem that requires special handling because it has led to the decline in resource quality. For that use spatial autoregressive Poisson models to detect factors that influence the number of people with malnutrition. Based on the results of this study was obtained that the factors that influence the number of people with malnutrition are spatial and non-spatial. Spatial factor that predispose to a particular location is the location of the neighbors. This means that the number of people with malnutrition in a region or a nearby location will affect the number of malnourished people in the surrounding locations. While non-spatial factors that influence the number of people with malnutrition are the number of families residing in the slums, the area of land use structure is not enough irrigation, the number of health workers living in rural or village, and the number of per capita gross regional domestic product. The coefficient of determination for the SAR Poisson model was 0.57. Based on the scan statistic method obtained four clusters of hotspot areas.
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Judul: Katalis Zink Ferit Tersubstitusi Cu untuk Oksidasi Benzil Alkohol
Abstrak: Katalis berperan dalam menurunkan energi aktivasi. Katalis ZnFe2O4 dan ZnFe2O4 tersubstitusi Cu disintesis menggunakan metode kopresipitasi untuk digunakan dalam oksidasi benzil alkohol. Katalis dicirikan dengan spektrofotometer inframerah (FTIR) dan difraktometer sinar-X. Spektrum FTIR menunjukkan puncak serapan dari ikatan Zn-O dan Fe-O masing-masing pada rentang 559‒575 cm-1 dan 419‒447 cm-1. Difraktogram menunjukkan terbentuknya fase tunggal Zn0.6Cu0.4Fe2O4 pada katalis ZnFe2O4 tersubstitusi Cu 20%. Benzil alkohol dioksidasi dengan sistem refluks. Konversi benzil alkohol meningkat dengan meningkatnya suhu. Kadar benzaldehida yang dihasilkan dengan katalis ZnFe2O4 tersubstitusi Cu 20% lebih tinggi dibandingkan dengan katalis ZnFe2O4 yang tidak tersubstitusi dan yang tersubstitusi Cu 60 dan 80%. Hal tersebut menunjukkan bahwa katalis ZnFe2O4 tersubstitusi Cu 20% memiliki kinerja yang lebih baik dalam oksidasi benzil alkohol menjadi benzaldehida dengan kadar benzaldehida 8.763%.
Keyword: Benzil alkohol, katalis, oksidasi, ZnFe2O4