Judul: Masa Simpan Tempe Segar Berbumbu dengan Metode Vakum dan Suhu Penyimpanan
Abstrak: Tempe merupakan salah satu warisan budaya bangsa Indonesia. Improvisasi pengolahan tempe diperlukan untuk meningkatkan nilai tempe sebagai komoditas ekspor. Salah satu caranya ialah membuat tempe segar berbumbu. Tempe segar berbumbu dapat dibuat dengan cara mencampurkan bumbu setelah kedelai dimasak untuk kedua kalinya, ketika kedelai masih hangat dan basah, sebelum dilakukan peragian. Ada dua formula bumbu yang terpilih setelah melalui penelitian tahap I. Formula pertama ialah bumbu yang terbuat dari kombinasi garam, bawang putih bubuk, dan ketumbar bubuk; dan yang kedua ialah bumbu yang terbuat dari kombinasi garam, bawang putih bubuk, dan cabai bubuk. Masa simpan tempe segar berbumbu yang mendapat perlakuan blansir dan pengemasan vakum dapat diperpanjang hingga 3 hari jika dismpan pada suhu ruang, 8 hari pada suhu refrigerator, dan 28-29 hari pada suhu freezer.
Keyword: Masa simpan, Pemvakuman, Suhu Penyimpanan Tempe segar berbumbu.

Judul: Peningkatan Umur Simpan Tempe Bacem dengan Metode Vakum pada Beberapa Kondisi Penyimpanan
Abstrak: Tempe bacem is a kind of tempe product that has spicy and sweet taste. The shelf life of this product is very short. The objective of this research was to increase tempe bacem’s shelf life by vacuum and non-vacuum packaging that stored in different conditions. Tempe that used in this research was in round form with 5 cm of diameters and 1 cm of thick. Tempe bacem was processed by selected formula, packaged by vacuum and non-vacuum and then stored in room condition (26-300C) and refrigerator condition (100C). Total protein in this product was 33.7% (db), fat 0.9% (db), ash 3.0% (db), carbohydrate 66% (db), while antioxidant capacity was 194.65 mgAEAC/100 g tempe. The pH value in tempe bacem was 5.7, lightness 35.9, hardness 2848.5 gram force, total TPC 1.8x103 CFU/g and total koliform <3.0 MPN/g. The shelf life of tempe bacem that packaged by non-vacuum method in room condition was 2 days, and in refrigerator condition was 6 days. The shelf life of tempe bacem that packaged by vacuum method in room condition was 3 days and in refrigerator condition was 18 days.
Keyword: Bogor Agricultural University (IPB), tempe bacem, shelf life, vacuum packaging

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