Judul: Karakteristik Mikrobiologis Bakso Sapi yang Diawetkan dengan Substrat Antimikroba dari Lactobacillus plantarum 1A5 pada Penyimpanan Suhu Ruang.
Abstrak: Daging merupakan pangan sumber protein hewani yang memiliki kandungan nutrisi lengkap terutama komposisi proteinnya. Diversifikasi dari produk daging salah satunya adalah dengan dibuat bakso. Daging memiliki karakteristik mudah rusak (perishable) oleh aktivitas mikroorganisme. Salah satu cara yang biasa digunakan oleh produsen bakso untuk memperpanjang umur simpan adalah dengan penambahan bahan pengawet berbahaya untuk kesehatan seperti formalin dan boraks. Konsumen sekarang sudah cenderung untuk lebih memilih produk makanan yang berpengawet alami yang tidak menimbulkan masalah kesehatan. Bahan pengawet ini berasal dari salah satu bakteri asam laktat. Bakteri asam laktat mampu menghasilkan asam laktat, hidrogen peroksida dan hasil metabolisme lain yang dapat memberikan pengaruh positif bagi yang mengonsumsi.
Keyword: 

Judul: Karakteristik Mikrobiologis Bakso Sapi  yang Diawetkan dengan Substrat Antimikroba dari  Lactobacillus plantarum  1A5 pada Penyimpanan Suhu Ruang
Abstrak: The objective of this research was to observe the effect of antimicrobial substrate of Lactobacillus plantarum 1A5 origin as a natural preservative on beef meatball at room temperature storage. The complete randomized design of two factors was used to analyze the effect of antimicrobial substrate microbiological characteristics of beef meatball. The treatments and control were storage at room temperature (± 280 C) with antimicrobial substrat 100 % and 0 % for 0 hour, 9 hours, and 18 hours after conditioning process. The variables were total plate count, quantitative of Staphylococcus aureus, quantitative of Escherichia coli and qualitative of Salmonella spp. The result of this research showed that there were decreasing (P<0,05) of total microbe, quantitave of Staphylococcus aureus and quantitative of Escherichia coli were decreasing (P<0,05) during room storage until 9 hours. Salmonella spp. test showed negative result.
Keyword: 

Judul: Which one use to overcome multicollinearity ? Ridge regression or principal component regression
Abstrak: In multiple linear regression the effect of nonorthogonality of the prediction vectors is to pull the least squares estimates of the regression coefficient away from the true coefficient that one is trying to estimate. Separating the effects of each factor of the regressors has been found to be difficult because of high correlation among independent variables. Although the multicollinearity problem has been recognized by many ways, ordinary least squares is still the main technique used in many fields, but the estimation of regression coefficient can present problems when the data vectors for the predictors are not orthogonal, or in this case are multicollinear. In using ordinary least squares, the regression coefficient may be unstable, too large in absolute value, and incorrect with respect to sign. Introduced biased estimation refer to unbiased one that measure the average closeness to the parameter being estimated corresponding to its mean square error (MSE). Ridge regression and principal component can produce better estimates by its own way than those given by ordinary least squares. It may well be preferred estimator since it will have larger probability of being close to true parameter value. Two of the biased regression methods, ridge regression and principal component regression will be applied into this research and discuss briefly for each of its properties. These methods have its particular idiosyncrasy. The biased regression methods attack the collinearity problem by computationally suppressing the effects of the collinearity. Ridge regression does this correctly by modifying the correlation matrix adding a biased constant driving through nonsingular matrix. Principal component regression attacks the problem by regressing Y on the important principal component and then parceling out the effect of the principal component variables to the original variables. Finally translated back the obtained variables into original variables yield the same sign and nearly the same coefficients for both methods.
Keyword: Regresssion analysis