Judul: Penerapan bayesian network untuk memprediksi pemenang Pilkada
Abstrak: Quick count mempakan metode untuk memprediksi hasil pemilihan melalui kajian penarikan contoh. Ketika quick count memenuhi 3 hal mendasar yaitu independency lembaga pelaksana nonpartisan, metode penarikan contoh yang tepat, dan implementasi hasil yang baik, maka quick count dapat memprediksi hasil perhitungan KPUD. Pelaksanaan quick count diawali oleh sejumlah survei dukungan Pilkada. Survei dukungan Pilkada dapat memprediksi hasil quick count. Perbedaan basis pengamatan quick count dan survei dukungan Pilkada menjadikan analisis pendugaan pemenang Pilkada tidak dapat dilakukan secara langsung, sebingga harus melalui proses data mining. Penelitian ini menggunakan Bayesian network yang merupakan salah satu teknik data mining untuk membangun model prediksi. Bayesian Network menghasilkan model peluang interaktif yang dapat memprediksi pemenang Pilkada berdasarkan kriteria kombinasi peubah. Penyusunan network dilakukan dengan metode simple narve Bayesian dan model causal network.
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Judul: Model bayes untuk pendugaan area kecil dengan penarikan contoh berpeluang tidak sama pada kasus respon binomial dan multinomial
Abstrak: In this research a Bayesian Method of Small Area Estimation (SAE) has been developed based on binomial and multinomial response variables using Susenas data obtained from unequal probability sampling. Case study was carried out to predict education level of the population measured by literacy rate and mean years of schooling in sub-district level in East Java Province. The SAE model for binomial response was developed with two methods, i.e. using weighted logit normal mixed model and involving the probability of sampling selection model as exponential function into the SAE model. A simulation study was carried out by implementing 100 times sampling selection into population data. Penalized Quasi Likelihood (PQL) and Restricted Maximum Likelihood method (REML) was used to parameter estimation of SAE model. Based on the simulation result, we found that the weighted logit normal mixed model gave the best estimate. In application, the weighted logit normal mixed model also provided good prediction of literacy rate in Sumenep and Pasuruan regency.For the multinomial respons, we applied the weighted logit multinomial mixed model. MSE estimation was used Jackknife method and it gave very small MSE of about 1,14 x10-7.
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Judul: Characterizations of low alloy steel sae 4422  plated with chromic acid (Cro3)
Abstrak: Characterizations of low alloy steel SAE 4422 plated with chromic acid (CrOrO3) was use Optical Microscopy (OM), Scanning Electron Microscopy (SEM), X-Ray Diffraction (XRD), RD),Energy Dispersive Analysis of X-Ray (EDAX), Hardness Test and Roughness Test. Microstructures analysis exhibits cracks and grain boundaries microcracks as a characteristics of chrome metal found at plating metal. Perlite and Ferrite as grains of steel found at base metal with grain boundaries around it. Phase analysis of plating metal appropriate to chrome metal with lattice constant (a) is 2.559 Å and phase analysis of base metal appropriate to iron ferrite with lattice constant (a) is 3.318 Å. Both plating and base metal have cubic system. Chemical analysis of plating metal at surface section exhibits that cracks area have highest value of carbon element compare with grains and grains boundaries area. While chemical analysis of base metal at cross section exhibits that perlite have highest value of carbon element compare with ferrite and grain boundaries. The hardness value of plating metal at surface section and cross section are 753 and 637 VHN. While, the hardness value of base metal and interface area at cross section are 205 and 373 VHN. The roughness value at measurement points of chrome surface for as received sample are 0.445 μm. Observation used Optical Microscopy and Scanning Electron Microscopy exhibits surface irregularities combine to form surface texture of plating metal at surface section.
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