Judul: Sifat fisik, kimia dan organoleptik salami daging domba yang menggunakan ultur kering umur 15 hari
Abstrak: Meat is one of food from animal product which is important for human being to fulfill requiremenr of animal protein. Lamb is one of meat consume in Indonesia. One of type of meat product is fermented sausage quality. Lactic Acid Bacteria of Lactobacillus plantarum and Lactobacillus fermentum were served to purpose of starter culture to yield the fermented sausage.
Keyword: 

Judul: Aplikasi penggunaan kultur starter kering yang disimpan selama 15 hari dalam pembuatan salami daging sapi
Abstrak: Meat is a foodstuff that our body needed due to its nutritional value, such as protein, essential fatty acid, mineral and vitamin. It is needed to all of organism to maintain their growth, include microorganism growth. Therefore, meat is a perishable food. The processing of meat are important thing to prolong the self life of meat from microorganism deterioration and to diversify food product. One kind of mean processed produk is salami. Salami is non-emulsionsausage made from the mixture of ground meat and fat with starter culture and spices, and then stuffing into casing, starter culture that used has important role to the salami product,
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