Judul: Kajian Pengaruh Konsentrasi Bahan Pengawet dan Jenis Kemasan Terhadap Daya Simpan Jamur Tiram (Pleuratus ostreatus) Segar Pada Penyimpanan Suhu Rendah
Abstrak: Kerusakan jamur tiram putih dapat disebabkan oleh mikroorganisme, reaksi biokimia (pencoklatim enzimatis) dan kimia (pencoklatan nonenzimatis) serta kerusakan fisik. Jamur tiram putih yang tidak diberi perlakuan (dibiarkan pada suhu ruang) hanya dapat bertahan satu hari dan setelah itu tidak layak lagi untuk dikonsumsi. Sulfur dioksida dan garamnya merupakan bahan pengawet yang dapat menghambat reaksi pencoklatan dan enzimatik. Pengawetan dengan suI fit akan memberikan ketahanan wama dan menghambat pertumbuhan serangga, kapang dan khamir.
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Judul: Kajian pengaruh konsentrasi bahan pengawet dan jenis kemasan terhadap daya simpan jamur tiram putih (Pleurotus ostreatus) pada penyimpanan suhu rendah
Abstrak: Jamur tiram putih (Pleurotus ostreatus) merupakan jamur pangan yang cukup baik kandungan gizinya. Kandungan iemak jamur tiram putih antara 1.08 - 9.4 %. Kandungan protein bervariasi dari 8.9 38.7 %. Total asam amino esensial pada jamur tiram putih adalah 33.5g/100g protein kasar. Jamur tiram putih merupakan sumber beberapa vitamin, meliputi thiamin, riboflavin, niasin, biotin dan asam askorbat. Kerusakan jamur tiram putih dapat disebabkan oleh mikroorganisme, reaksi biokimia (pencoklatan enzimatis) dan kimia (pencoklatan nonenzimatis) serta kerusakan fisik. Jamur tiram putih yang tidak diberi perlakuan (dibiarkan pada suhu ruang) hanya dapat bertahan satu hari dan setelah itu tidak layak lagi untuk dikonsumsi. Sulfur dioksida dan garamnya merupakan bahan pengawet yang dapat menghambat reaksi pencoklatan dan enzimatik. Pengawetan dengan sulfit akan memberikan ketahanan serangga, kapang dan khamir. warna dan menghambat pertumbuhan..dst
Keyword: jamur tiram putih, botani, perkembangbiakan, kandungan gizi

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