Judul: Penentuan Umur Simpan Sosis Sapid an Sosis Ayam dalam Kemasan Vakum dengan Model Arrhenius
Abstrak: Penelitian ini bertujuan untuk menentukan umur simpan sosis sapi dan sosis ayam dalam kemasan vakum melalui evaluasi perubahan sifat fisik, kimia, mutu mikrobiologi, dan penelitian organoleptik, serta penerapan model Arrhenius terhadap parameter pH dan Nilai TBA sosis. Penelitian ini diawali dengan mempelajari proses produksi sosis di PT. Tirta Ratna Unit Badranaya, Bandung. Secara umum pembuatan sosis meliputi penanganan awal daging, penghancuran daging, penambahan bahan tambahan seperti gula, garam, fosfat, isolat kedele, skim, tapioka, karagenan, es, dan bumbu-bumbu, pembentukan emulsi, serta pengemasan.
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Judul: Aplikasi kinetika arrhenius untuk pendugaan umur simpan dendeng sapi giling
Abstrak: Daging merupakan bahan pangan yang sangat mudah mengalami proses kerusakan (perishable food). bentuk awetan tradisional di daging Salah satu Indonesia adalah dendeng yang diproduksi secara meluas dan dikonsumsi oleh berbagai lapisan masyarakat. Proses pembuatan dendeng sapi giling termasuk kombi nasi antara proses "curing" dan pengeringan. Menurut Purnomo et al. (1983) dendeng termasuk dalam katagori "intermediate moisture meat product". Pendugaan umur simpan produk dendeng sapi giling akan sangat membantu produsen untuk mengetahui secara tepat masa kedaluwarsa produk yang dipasarkan dan akan membantu konsumen untuk memilih produk yang masih dapat dikonsumsi secara aman. Tujuan penelitian ini adalah untuk memperkirakan umur simpan dendeng sapi giling dengan menerapkan kinetika Arrhenius yang dinyatakan dalam konstanta laju peningka- tan kadar air, laju penyerapan oksigen dan laju perubahan warna dendeng sapi giling. ...
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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