Judul: Analisis Regresi Logistik Spasial untuk Menduga Status Kemiskinan Desa di Kabupaten Majalengka
Abstrak: Rural poverty status is influenced by several factors that are correlated between one rural area and the other. Logistic regression modeling with spatial weights is needed to determine the factors that affect rural poverty hotspots that are spatially correlated. Categorization of rural areas into hotspot (1) and non hotspot (0) was based on a hotspot Upper Level Set (ULS) Scan Statistic method which obtained 187 hotspot rural areas and 149 rural non hotspot. Rural hotspot areas have characteristics similar to non rural hotspot areas in various aspects. Logistic regression models with Spatial variable enhances logistic regression model without Spatial variable in predicting the status of rural poverty areas in Majalengka. Logistic regression models with Spatial variable has a higher correct classification rate (CCR) compared to logistic regression without Spatial variable. The explanatory variables that have a significant influence towards the status of rural poverty are Percentage of Farm Worker Families variable and Spatial variable.
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Judul: Penerapan Regresi Logistik Ordinal Spasial Untuk Menduga Status Kemiskinan Kabupaten Di Pulau Jawa
Abstrak: Analisis regresi logistik ordinal spasial merupakan analisis yang menduga pengaruh variabel penjelas terhadap variabel respon yang berupa data ordinal dengan ditambahkan unsur spasial di dalamnya. Pengaruh Spasial yang dimaksud adalah adanya matriks kebertetanggaan antar kabupaten yang akan diperhitungkan ke dalam model regresi logistik ordinal. Variabel respon berisi data berskala ordinal berupa enam tingkatan kemiskinan. Kemiskinan suatu daerah tidak lepas dari daerah di sekelilingnya, hal ini menunjukan adanya korelasi spasial yang perlu diteliti lebih lanjut. Penelitian ini bertujuan untuk mengetahui faktor-faktor yang mempengaruhi tingkat kemiskinan kabupaten di Pulau Jawa berdasarkan model regresi logistik ordinal spasial dan untuk membandingkan model regresi logistik ordinal spasial terhadap model regresi logistik ordinal non spasial. Data yang digunakan dalam penelitian ini adalah data sekunder, data variabel penjelas diperoleh dari data Potensi Desa (PODES) dan data Survei Sosial Ekonomi Nasional (SUSENAS) tahun 2005 sedangkan data variabel respon diperoleh dari hasil hotspot kemiskinan kabupaten di Pulau Jawa pada tesis Dian Kusumaningrum tahun 2010. Hasil analisis menunjukan bahwa model regresi logistik ordinal non spasial memiliki nilai Correct Classification Rate (CCR) sebesar 51.85%, sedangkan model Regresi logistik ordinal spasial memiliki nilai CCR sebesar 55.56% dengan besarnya sumbangan keragaman dari variabel spasial sebesar 43.056. Jadi dapat disimpulkan bahwa model regresi logistik ordinal spasial lebih baik dari pada model non spasialnya. Ada empat variabel yang berpengaruh terhadap tingkat kemiskinan suatu kabupaten di Pulau Jawa yaitu persentase desa perdagangan, persentase desa jasa, persentase keluarga tanpa listrik dan variabel spasial kemiskinan kabupaten.
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Judul: Synthesis of Apatite - Chitosan Composite Using Duck Eggshell and Chitosan with Temperature and pH Control
Abstrak: Composite of apatite-chitosan was prepared by using chitosan as polymer matrix. Calcium phosphate was prepared from CaO and (NH4)2HPO4, which the ratio Ca : P had to be 1.67. Chitosan is produced by deacetilase from biopolymer chitin. Duck eggshell (95% CaCO3) as starting material was converted to be calcium oxide (CaO) through calcinations with temperature 1000oC for 5 hours. In-situ and ex-situ methods were prepared from apatite and chitosan (dissolved with acetate acid 2%). Calcium phosphate prepared for bone implantation, but it is too fragile and brittle to be implant into the body. So addition of chitosan can decrease crystalinity and increase mechanic characteristic (tougher). Composite apatite-chitosan was resulted by fast precipitation. Fast precipitation was held in a breaker glass with nitrogen atmosphere, under controlled temperature at 37oC and pH 7, in order to increase amount of hydroxyapatite. Precipitation with CaO which was dropped by (NH4)2HPO4. Hydroxyapatite and chitosan were appearing at samples with in-situ and ex-situ methods which indicating by peak matching of XRD (closed lattice parameter, crystal size), overlapping at FTIR spectra and supporting by SEM picture (homogenous and agglomerated particle). However, it still needs further research to examine biocompatibility osteoconductive degree of sample by in-vivo and in-vitro test.
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