Judul: Model otoregresi simultan dan otoregresi bersyarat untuk analisis kemiskinan di Provinsi Jawa Timur
Abstrak: Poverty is one of the biggest problems in Indonesia. An approach to overcome this problem is determining the factors that affect poverty usually using ordinary least square regression model. However, poverty is not only influenced by explanatory variables but also poverty at surrounding locations. Therefore, this research employed spatial autoregressive models, i.e. Simultaneously Autoregressive (SAR) and Conditional Autoregressive (CAR). Spatial weighting matrix used in this study is the contiguity matrix. The statistics used for selection criteria model are the Akaike Information Criterion (AIC), the significancy of coefficient regression and variance parameters. The results show that they have same quality for spatial autoregressive models. 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 that getting healthy insurance, the percentage of people that getting subsidized rice, and the percentage of people that have poverty letter. LISA shows that hotspot of poverty on the island of Madura.
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Judul: Bayesian Simultaneous Autoregressive Models of Proverty Analysis in East Java Province
Abstrak: Simultaneous Autoregressive (SAR) is a spatial model derived from the linear regression equation that the error is designed as an autoregressive model which is the response variable at one location simultaneously observed to the response variable at other locations. In a previous study, Meilisa (2010) used maximum likelihood methods (MLM) without considering the initial information (prior) in the parameter estimates. The Bayesian approach is very good for smallsized samples, but it has not been much used or explored. According to Oliviera and Song (2008), based on simulation results showed that estimate value by Bayesian approach was closer to actual value, whereas by MLM lower than actual value. In this research, the noninformative prior that used for the Bayesian method wass the independence Jeffreys, Jeffreys-rule, and the uniform prior. The dataset that used was percentage poverty of 38 districts/towns in East Java province on the basis of explanatory variables were percentage of household that did not use clean water, residents who get health insurance and subsidized rice. The results showed that the spatial correlation of the three noninformative priors was 0.10. The best prior for SAR Bayes model with Jeffreys-rule prior had a coefficient of determination of 83.89% and it had the smallest value of variance estimator and Bayesian Information Criterion (BIC). In this case study, SAR Bayesian model had almost same coefficient determination with the results of regression and SAR model.
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Judul: Infeksi Virus Parainfluenza Pada Anjing
Abstrak: Virus parainfluenza anjing termasuk famili Paramyxoviridae, berbentuk pleomorf dengan ukuran bervariasi (bergaris tengah antara 100-500 nm), berpembungkus peka ether, berisi genom RNA dan secara serologis mempunyai kesamaan dengan virus SV-5 kera; dikenak sebagai salah satu kausa penting dari sindrom "kennel cough". Infeksinya dapat mengenai anjing semua umur yang belum pernah terinfeksi atau mendapatkan vaksinasi mengakibatkan gejala klinis ringan yang terbatas pada traktus respirasi atu infeksi yang secara klinis tidak memperlihatkan gejala nyata. Gejala klinis yang nampak setelah masa inkubasi (1-5 hari) terlewati berupa batuk yang tidak berat selama beberapa hari, berbagai tingkat tonsilitis dan pharyngitis dan kadang-kadang ditemukan kenaikan temperatur rektal antara 1-2 derajat C dari temperatur normal rat-rata. Anjing terinfeksi mengandung virus pada traktus respirasi bagian atas dan bawah serta limfonoduli sampai hari ke 9 setelah infeksi. Penyebaran virus antar anjing terjadi secara aerogen.
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