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: Kajian Perbandingan Metode Least Median of Squares dan Maximum Likelihood Type pada Regresi Kekar
Abstrak: Keberadaan pencilan dapat memengaruhi pendugaan parameter dalam analisis regresi yang menggunakan metode ordinary least squares (OLS). Metode regresi kekar dapat digunakan sebagai pendekatan alternatif dari OLS. Penelitian ini dilakukan untuk mengkaji perbandingan dari dua metode regresi kekar, yaitu least median of squares (LMS) dan maximum likelihood type (M) melalui data simulasi. Data simulasi diperoleh dengan membangkitkan bilangan acak berdasarkan model regresi linear berganda dua peubah penjelas. Kombinasi yang digunakan adalah jenis pencilan, proporsi pencilan, dan ukuran contoh. Berdasarkan nilai bias mutlak dan kuadrat tengah galat (KTG) diperoleh bahwa metode M lebih tepat diterapkan ketika data mengandung vertical outlier atau good leverage point, lalu diikuti oleh metode LMS. Sementara itu, metode LMS dapat menduga parameter regresi pada data yang mengandung bad leverage point. Analisis selanjutnya dilakukan pada data pertanian mengenai pengaruh pupuk organik dan konsumsi beras per kapita terhadap produksi padi sawah di 34 provinsi tahun 2017. Hasil yang diperoleh menunjukkan bahwa metode LMS menghasilkan nilai bias mutlak dan KTG terkecil., The presence of outliers can be affect to estimate the parameters of the linear regression using ordinary least squares (OLS). The robust regression methods can be used as an alternative to OLS. This study aims to compare the performance of two methods, namely least median of squares (LMS) and maximum likelihood type (M) using simulation study. The simulation data obtained by generating random number based on multiple linear regression with two explanatory variables. With combination of the sample size, outliers type, and the number of outliers. Based on the criteria: absoulut bias and mean square error (MSE), the M-estimator gave better result than the LMS-estimator when the data contains vertical outliers or good leverage point. Meanwhile, the LMS-estimator gave better result at data containing bad leverage points. The next step is analysed data application for agriculture on the affect of organic fertilizer and rice consumption per capita against the use of rice production in 34 provinces in 2017 using the best method based on data characteristic. The results from LMS-estimator produce the smallest absolut bias and MSE.
Keyword: LMS, M, OLS, Outlier, Robust Regression

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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