Judul: Penerapan Regresi Spasial pada Data Kemiskinan Kabupaten/Kota di Provinsi Jawa Barat Tahun 2012
Abstrak: Poverty is a social problem that normally occurs in the middle of society, especially in developing countries. One important aspect to support poverty reduction strategy is the availability of accurate poverty data. Good data can be used to evaluate government policies on poverty, compare poverty between regions and determine the targeted destitute population with an am to improve their living conditions. One effort to overcome poverty is to identify the variables that affect it. In this research, poverty issue is examined by percentage of poor people districts/cities in West Java Province in 2012, making use of spatial regression analysis. The analysis showed that the spatial lag models (SAR) produced better allegation parameters than classic regression models in case of spatial correlation. This can be seen from the smaller AIC of SAR models (129.62) than classic regression models (133.06). In SAR models, variables that significantly affect the percentage of poor people in district/cities are the percentage of households using clean water (X10) and households with floor area per capita (X21).
Keyword: Bogor Agricultural University (IPB), spatial lag models (SAR), Poverty

Judul: Model regresi spasial untuk deteksi faktor-faktor kemiskinan di Provinsi Jawa Timur
Abstrak: Poverty is one of the biggest problems in Indonesia. An approach to overcome this problem is to determine the factors that affect poverty usually using ordinary least square regression model (OLS). However, poverty is not only influenced by explanatory variables but also by various aspects related to surrounding locations. Therefore, this research employed spatial regression models, i.e. Spatial Autoregressive Models (SAR), Spatial Error Models (SEM), and Spatial General Models (SGM). Contiguity matrix is as spatial weighting matrix. The model selection criteria are the coefficient of determination (R2), slope regression of dependent variable to its estimator and the value of RMSE (Root Mean Square Error). The results show that SAR is better regression model than OLS and the factors that affect poverty are the percentage of people who did not complete primary school (SD), the percentage of people who drink another kind of water instead of drinking water, and the percentage of people who live in unhealthy houses with floor area at least 8 m2 per capita.
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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