Judul: Pengembangan Prototipe Spatial Data Mining Untuk Karakterisasi Desa Miskin di Jawa Barat
Abstrak: Data mining, atau secara populer dirujuk sebagai Knowlegde discovery in databases (KDD) adalah teknik yang secara otomatis. melakukan ekstraksi pola yang merepresentasikan pengetahuan yang secara implisit tersimpan dalam basisdata besar, data warehouse, dan tempat penyimpanan yang masif lainnya. Spatial data mining berurusan dengan pencarian pengetahuan dalam basisdata spasial.Salah satu teknik dalam spatial data mining adalah karakterisasi spasial (spatial characterization). Karakterisasi spasial digunakan untuk mengungkap karakteristik atau deskripsi ringkas spasial dari sifat-sifat yang menarik pada sebuah komunitas (obyek target) tetapi tidak untuk keseluruhan basisdata. Dalam penelitian ini dilakukan pengembangan prototipe sistem yang dapat menerapkan teknik spatial data mining untuk mengungkap karakteristik spasial dari kondisi kemiskinan di Jawa Barat. Prototipe yang dihasilkan terdiri dari empat komponen yaitu modul pengolahan awal, modul spatial data mining, modul basisdata dan modul visualisasi. Modul spatial data mining tersusun atas neighbourhood graph, predicate filter, neighbourhood index dan algoritma karakterisasi spasial.
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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: Evaluation of Influenza Antibody Titers in Serum and Egg Yolk of Layers Vaccinated with Influenza Vaccines.
Abstrak: Influenza belongs to the Orthomyxoviridae family which causes upper and lower respiratory tract infection. The aim of this study was to evaluate influenza-A specific antibody titers from serum and egg yolk of hens vaccinated with quadrivalent influenza vaccines. Three Hy-line Brown layers were vaccinated with quadrivalent influenza type A and B inactivated vaccines. Vaccination was carried out three times at a dose of 0.25 ml/chicken via the intramuscular route. The first vaccination was carried out at 24 weeks of age, then boosters were performed at 28 and 32 weeks of age. Serum and egg yolk were collected every week for 20 weeks since the first vaccination. Influenza A H3N2 and H1N1 antibody titers were determined using hemagglutination-inhibition (HI) test and agar-gel-precipitation (AGP) test. Influenza-A H3N2 and H1N1 specific antibodies were detected in both serum and egg yolk from the first to twenty weeks after the initial vaccination. Highest HI geometric mean titer (GMT) of H1N1 antibody reached 26.67 HIU at nine weeks post first vaccination (p.f.v), while H3N2 antibody reached 26 HIU at six weeks p.f.v. Our result showed that influenza antibodies can be produced in layers using quadrivalent vaccines and layer chickens are able to produce equivalent antibody levels for each influenza subtype.
Keyword: antibody, egg yolk, influenza, layer chickens, serum