Judul: Memperpanjang umur simpan bunga krisan potong (Chrysanthemum sp.) tipe yellow fiji dengan perlakuan pra penyimpanan, suhu dan komposisi larutan pulsing untuk mempertahankan kesegaran selama penyimpanan
Abstrak: Saat ini bisnis dibidang pertanian merupakan salah satu usaha yang masih dapat dipertahankan dan menjadi andalan untuk mempertahankan perekonomian Indonesia. Salah satu bisnis pertanian tesebut adalah dibidang hortikultura. Bunga krisan (Chrysanthemum sp.) merupakan salah satu jenis bunga potong yang digemari konsumen baik dalam negri maupun luar negri. Permintaan bunga potong untuk wilayah Jakarta meningkat dengan laju sebesar 10% per tahun . Bunga potong yang dipasarkan tentunya harus mempunyai kualitas yang baik. Salah satu kualitas yang diharapkan adalah umur kesegaran (vase life) bunga potong yang cukup lama.
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Judul: Sistem Penunjang Keputusan Perencanaan dan Pengendalian Prodaksi Bunga Krisan Potong (Chrysanthemum, sp.) di PT. Saung Mirwan. Bogor
Abstrak: Tanaman hias dan bunga (florikultura) merupakan salah satu komoditas andalan Indonesia. Potensi florikultura ternyata tak kalah dengan buah·buahan dan sayuran. Hal tersebut dapat dilihat dari perkembangan ekspornya dari tahun ke tahun serta besarnya permintaan di pasar dalam negeri. Pada tahun 1995 volume ekspor florikultura Indonesia sebesar 698 ton dan mencapai volume 1.531 ton di tahun 1999 (Deptan, 2002). Sedangkan untuk permintaan komoditas florikultura di dalam negeri diperkirakan tumbuh antara 15 - 20% per tahun sehingga diperkirakan pada tahun 2005 permintaan dalam negeri meneapai Rp 186 - 428 miliar (Bank Indonesia, 2002).
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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