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.
Keyword: 

Judul: Penerapan Regresi Terboboti Geografis untuk Data Produk Domestik Regional Bruto (Studi Kasus: 113 Kabupaten/kota di Pulau Jawa Tahun 2010)
Abstrak: Java plays an important role for Indonesia’s economic growth because its Gross Domestic Regional Product (GDRP) contributies 58.7 % of Indonesias national Gross Domestic Product (GDP). The Districs of Java has a diverse structure of GDRP. Therefore it is difficult to fulfill the assumption of variance homogeneity in linier regression analysis. Hence, regression model isn’t appropriate. Geographycally Weighted Regression (GWR) is one of the models which can be used to solve this problem. In the case of Java’s GDRP of 2010, it can be concluded that GWR model is better than linier regression model based on Sum Square Error (SSE), Akaike Information Criterion (AIC), and R2. Globaly, there are 3 factors effecting GDRP in Java which are the percentage of poor people, the amount of families using electricity, and the ratio of total population and the amount of education facilities
Keyword: 

Judul: Nanokristalin Pirit (FeS2) sebagai Material Semikonduktor Fotovoltaik.
Abstrak: Teknologi fotovoltaik adalah suatu proses yang dapat mengubah sinar matahari menjadi energi listrik dengan media semikonduktor. Penggunaan sel surya untuk fotovoltaik hanya dapat mengonversi radiasi matahari menjadi energi listrik kurang dari 20%. Efisiensi akan meningkat ketika material semikonduktor untuk fotovoltaik dibuat menjadi ukuran nano. Pirit merupakan salah satu material semikonduktor fotovoltaik yang memiliki energi pita celah (Eg) dalam kisaran 0.8–0.95 eV. Energi pita celah tersebut terbukti meningkat ketika disintesis dengan metode hidrotermal menggunakan beragam molar [S]:[Fe] pada suhu 200 °C dalam 24 jam. Nilai energi pita celah yang dihasilkan adalah 1.58–2.073 eV. Ukuran nanokristalin pirit yang dihasilkan berada pada kisaran 21.04–58.77 nm menurut data Particle Size Analyzer. Besarnya ukuran nanokristal pirit yang tumbuh kurang dari 50 nm merupakan poin penting untuk aplikasi fotovoltaik.
Keyword: Hidrotermal, fotovoltaik, nanokristal, pirit