Judul: Mempelajari Pembuatan "Cider" Nangka (Artocarpus heterophyllus Lmk.)
Abstrak: Buah nangka (Artocarpus heterophyllus lmk.) merupakan salah satu buah tropik yang dihasilkan Indonesia. Buah ini mempunyai rasa yang manis dan bau yang harum, sehingga sering dikonsumsi dalam bentuk segar. Daging nangka selain dapat diolah menjadi berbagai macam makanan seperti dodol, wajik dan lain-lain dapat juga dijadikan minuman, salah satunya adalah 'cider' Nangka yaitu minuman fermentasi dari sari buah nangka.
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Judul: Pembuatan cider belimbing (Averrhoa carmbola)
Abstrak: Buah belimbing banyak ditemukan di Indonesia, merupa- kan buah yang biasanya dikonsumsi segar. Pemanfaatan buah belimbing selain dikonsumsi segar masih sangat terbatas, yaitu sebagai manisan dan asinan. Salah satu pemanfaatan dari buah belimbing adalah dalam bentuk cider. Mikroba yang berperan pada proses fermentasi adalah khamir dan bakteri. Khamir yang digunakan dalam penelitian ini adalah Saccharomyces cerevisiae dan Saccharomycodes ludwigii, berfungsi mengkonversi gula menjadi alkohol, serta bakteri Acetobacter xylinum yang berfungsi versi alkohol menjadi asam. mengkon- Secara singkat proses pembuatan cider belimbing sebagai berikut: pencucian, penyiangan, pemblenderan, penyaringan, pasteurisasi, pendingin, inokulasi, fermenta- si, pasteurisasi, pelayuan. Penelitian yang dilakukan adalah sebagai berikut: penelitian pendahuluan untuk menentukan kondisi fermentasi, dan batas lama fermentasi yang digunakan pada penelitian ...
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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