Judul: Model Regresi Terboboti Geografis Bayes untuk Data Kemiskinan (Kasus 35 Desa atau Kelurahan di Kabupaten Jember)
Abstrak: Bayesian Geographically Weighted Regression (BGWR) is locally linear regression method to solve some difficulties that arise in Geographically Weighted Regression (GWR) model, such as outliers or non-constant variance. The Bayesian approach solves the problems by producing estimates that are robust against aberrant observations. The aberrant observations are automatically detected and downweighted to mitigate their influence on the estimates. In this research, the weighting used for BGWR model is Gaussian and bi-square kernel function. The results showed that BGWR model is better than GWR model. According to mean square error (MSE) values and coefficient of determinant (R2), Gaussian kernel function is better than bi-square kernel function as BGWR weighting to analyze the data on average expenditure per capita of 35 villages in Jember Regency.
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Judul: Model Regresi Logistik Terboboti Geografis (Studi Kasus : Pemodelan Kemiskinan di Provinsi Jawa Timur)
Abstrak: Geographically Weighted Logistic Regression (GWLR) model is locally logistic regression model. The data in this model is assumed following Binomial distribution and the geographical factor is considered. The geographical factor is used to analyze spatial data from nonstationary processes. The basic idea of this model consider the geography or location as the weight in parameter estimation. The parameter estimator is obtained from Iteratively Reweighted Least Square method by giving different weight for different location. Model for determining the poverty level with a global logistic regression was not suitable to be applied in all districts of East Java Province, because it could be a predictor effect on the poverty level in the region but in other regions the predictor is not significant. The data in this research is from National Social Economy Survey 2008. This research will determine the factors that affect the poverty level in the East Java Province using logistic regression model and GWLR model with a weighting adaptive bi-square kernel function. The results showed that the classification accuracy of logistic regression model was 78.90% and the classification accuracy of GWLR model was 89.47%. GWLR model with a weighting adaptive bi-square kernel function was better than logistic regression model because it had the high classification accuracy and small AIC value.
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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