Judul: Kajian metode pendugaan pada model regresi dengan peubah penjelas bersifat acak
Abstrak: Regression analysis is a statistical method to evaluate the relationship between a variable with other variables. Model I regression analysis describes the one-way relationship between variable X and variable Y with the value of X as fixed variable or measured without error. Model I regression analysis uses Ordinary Least Square (OLS) as an estimation method. Model II regression analysis is a regression model with predictors variable that becomes a random variable. There are two estimation methods of model II regression analysis, ordinary least product regression (Model IIA) and major axis regression (Model IIB). The convenience of using model I regression analysis with OLS estimation method causes many researchers to use this model as a model regression analysis with random predictor variables. The purpose of this study is to compare model I regression analysis (OLS estimation method) and model II regression analysis (ordinary least product estimation method and major axis regression estimation method). The data used in this research are simulation data where response variable (Y) and predictor variable (X) are random. The result of this study showed for predictor variable is random, ordinary least product regression is the best estimation method compared to the other methods because it produces the smallest value of bias and the smallest value of mean square error. In this condition, OLS can be used for the estimate parameter of simple linier regression model if the correlation value between predictor variable (X) and response variable (Y) is high (r ≥ 0.9).
Keyword: Bogor Agricultural University (IPB), Model IIB, Model IIA, Model I, mean square error, correlation

Judul: Perbandingan Metode MKT, LTS, WIN, dan Theil pada Pendugaan Parameter Regresi Apabila Galatnya Menyebar Eksponensial
Abstrak: Parameter estimation in simple linear regression analysis can be done with Ordinary Least Square (OLS). This method requires the assumption that error must be Normally distributed. However, in reality error is not always Normally distributed, but has another distribution, for example Exponential. Concequently, parameter estimation by MKT is not optimal. This problem can be solved by applying robust regression. This research applied Least Trimmed Square (LTS) method, Winsorized Least Square (WIN) method, and Theil’s method as a solution. The data used in this study was simulation data. Explanatory variables (x) is generated from the Normal distribution, while the error generated from the Exponential distribution. The results of estimation are evaluated with MSE and Relative MSE. The results showed that LTS was the best method when the error is Exponentially distributed.
Keyword: Bogor Agricultural University (IPB), WIN, Theil’s Method, OLS, LTS

Judul: Membran Komposit Kitosan-Zeolit untuk Aplikasi Direct Methanol Fuel Cell
Abstrak: Direct methanol fuel cell (DMFC) is a fuel cell that utilize methanol as the fuel source. The electrolyte membrane used in the DMFC system in this research was chitosan-zeolite composite membrane. The composite membrane was synthesized with chitosan-zeolite composition variation of 1:1, 1:2, 1:3, and 2:1. The membranes were analyzed by Fourier transform infrared spectrophotometer (FTIR) and scanning electron microscope, and for the specific gravity. Membrane performances were tested with methanol uptake, methanol permeability, conductivity, and potential difference in the DMFC system. The FTIR spectrum showed no formation of new functional groups inducating that the composite membrane was a result of physical mixing. Morphological analysis showed that the membrane was nonporous. Chitosan-zeolite 1:3 composite membrane had the highest specific gravity and methanol uptake, 1.4226 g/mL and 41.44%, respectively. The highest proton conductivity was showed by the 1:2 chitosanzeolite composite membrane, namely 7.2946× 10-7 S/cm. Based on this research results, chitosan-zeolite composite membrane can be applied in DMFC system.
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