Judul: Mempelajari Sifat-Sifat Fisiko - Kimia Daging Paha Kodok (Rana catesbeiana) Beku
Abstrak: Penelitian i n i bertujuan mempelajari perubahan-perubahan f i s i k dan kimia daging paha kodok (m catesbeiana) akibat pengaruh kecepatan pembekuan dan perlakuan "postmortemgl selama penyimpanan beku. Pada penelitian i n i dilakukan pembekuan dengan dua cara, yaitu pembekuan cepat (dry i c e ) dan pembebuan lambat (deep freezer) dengan keadaan awal daging paha kodok prerigor (tanpa post-mortem) dan post-rigor (dengan post-nortem) s e r t a disimpan beku selama 8 minggu. Pengamatan dilakukan terhadap pola pembehuan, pH, drips, warna (kecerahan), daya i k a t air (ViHC), keempukan, kadar protein, struktur jaringan dan u j i Salmonella.
Keyword: 

Judul: Mempelajari Pengaruh Penambahan Enzim Papain secara Ante-Mortem terhadap Sifat Fisiko Kimia Daging Kambing Tua Jantan
Abstrak: Keempukan, citarasa, dan warna merupakan faktor kualitas utama daging bagi konsumen. Diantara ketiga ha1 tersebut, keempukan memegang peranan terpenting. Secara alami keempukan dapat dicapai dengan pemeraman. Penggunaan enzim merupakan cara lain untuk meningkatkan keempukan. Dalam penelitian ini kambing kacang tua jantan disuntikkan enzim sebanyak 0.5ml/kg berat hewan dengan aktivitas 1000 TU, melalui vena jugularis 10-15 menit sebelum menyernbelihan Daging paha belakang diperam selama 0,6, dan 12 jam pada I suhu 4°C. Daging ini kemudian dianalisa terhadap sifat fisik (warna dan keempukan), sifat kimiawi (pH, daya ikat air, dan profl cita rasa), dan organoleptik (wama, ke- I empukan, aroma daging, dan kesukaan).
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