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.
Keyword: 

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.
Keyword: 

Judul: Pengaruh Konsentrasi Katalis Methyl Ester Sulfonic Acid Terhadap Sifat Fisikokimia Gliserol Ester Oleat yang Dihasilkan
Abstrak: Glycerol is a by-product from palm olein biodiesel industry which could be value-added to obtain various products. One of the alternatives is to modify glycerol become glycerol ester oleat. Before processing, glycerol was first refined using 5% (v/v) phosphoric acid 85%. The refinery showed that glycerol content was increased from 49% to 80%. Glycerol was then esterified with oleic acid and catalyst methyl ester sulfonic acid (MESA) to obtain glycerol ester. Esterification was conducted at 240°C and nitrogen atmosphere using 200 rpm of mixing for 90 minutes. The best catalyst concentration used in esterification process is at 0,5% and resulting glycerol ester oleat with acid number 48,07 mg KOH/g sample, density of 0,9124 g/cm3, viscosity 68,32 cP (25°C), kinematic viscosity 36 cSt (40°C), flash point 230°C and pour point -15°C.
Keyword: glycerol ester, glycerol