Judul: Kajian data hilang dalam regresi linear berganda dengan metode missing indicator
Abstrak: Penelitian ini dilakukan dengan tujuan untuk mengetahui metode terbaik dalam pendugaan parameter pada regresi linear berganda dengan dua peubah penjelas X1 dan X2. Pada X2 terdapat data hilang secara acak sedangkan peubah respon (Y) dan X1 selalu teramati. Metodecomplete-case merupakan salah satu pendekatan yang sering digunakan untuk mengatasi data hilang, salah satunya data hilang dalam model regresi linear berganda. Metode complete-case menghasilkan penduga yang bersifat tak bias, tetapi kurang efisien jika jumlah data yang hilang cukup besar. Metode missing indicator merupakan pendekatan lain yang dapat digunakan dengan menambahkan peubah indikator pada peubah penjelas dalam model regresi. Metode ini terdiri dari metode missing indicator I dan metode missing indicator 2.
Keyword: 

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: Bioelectricity Production from Citrobacter freundii GBH253 and Chlorella pyrenoidosa on Microbial Fuel Cell Anode with Different Carbon Sources
Abstrak: Microbial Fuel Cell (MFC) development has significantly increased from various aspects to get higher and more stable electricity. Developing MFC on microorganism aspect is very important because it plays a crucial role in the MFC system. C. freundii GBH253 is one of the potential exoelectrogen bacterium that could be developed for the MFC system. Integrating C. freundii GBH253 and electricity-producing microalgae, such as C. pyrenoidosa, in the anode of MFC could get more stable and higher electricity. In this study, C. freundii GBH253 and C. pyrenoidosa were coupled to produce electricity from different substrates. C. freundii GBH253 and C. pyrenoidosa were tested on three different media types before being tested on MFC. They were also examined for the co-cultivation test to determine the symbiotic relationship between the two microorganisms. The MFC test was conducted using the Randomized Block Design Factorial and the Tukey test to see significant differences from each treatment. The parameters observed in this study were voltage, power density, Chemical Oxygen Demand (COD), Coulombic Efficiency (CE), pH, and volatile fatty acid (VFA). C. freundii GBH253 cannot grow on microalgae medium, while C. pyrenoidosa could grow on bacterial medium and a mixture of bacterium and microalgae media. The bacterium gained benefit from co-culture with microalgae. In the MFC test, electricity was generated in all treatments, which bacterium + microalgae with acetate treatment could generate power density up to 211,97 mW/m2 and the most stable compared to others. Production of electricity in bacterium-microalgae combination dominated by the bacterium; hence, microalgae still help bacterium by producing electricity and raised the COD value. The pH value of all treatments was higher than the initial pH. Acetate and phenol were VFA produced in all treatments, whereas butyric acid and propionic acid were found in some treatments. Pearson correlation showed that some VFA highly correlated with power density.
Keyword: Chlorella pyrenoidosa, Substrate, Microbial Fuel Cell, Electricity Producing Microalgae, Volatile Fatty Acid, Citrobacter freundii