Judul: Karakteristik Mutu Fisiko-Kimia Sosis Fermentasi Daging Sapi
Abstrak: Dalam penelitian ini, sosis yang dibuat merupakan campuran daging sapi, lemak sapi, garam kuring NPS (Nibit Poekelen Salz) dan bumbu-bumbu kemudian dibuat dalam bentuk cacaban dan dimasukan ke dslam selongsong sintetik berdiameter 21 mm selanjutnya difermentasi pada suhu ruang (+ 25 - 30%) selama 30 hari dengan kombinasi pengasapan dingin (T 5 30°C, 1 jam per hari) selama 7 hari di awal fermentasi. Fermentasi yang dilakukan sifatnya spontanlalamiah, tanpa penambahan starter kultur
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Judul: Karakteristik fisik, kimia dan organoleptik sosis daging sapi yang difermentasi dengan kultur starter kering umur simpan 30 dan 45 hari
Abstrak: The aim of this research was to observe the effect of dry starter culture addition in beef fermented sausage on physical characteristic (pH, aw, toughness) and chemistry characteristics. This research used dry culture starter with different of timme storage (30 and 45 days).
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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