Judul: Produksi ikan neon tetra (paracheirodon innesi( ukuran M dengan padat tebar 25,50,75 dan 100 ekor/liter dalam sistem resirkulasi
Abstrak: Ikan hias merupakan salah satu komoditas perikanan yang memiliki nilai ekonomis tinggi dan termasuk komoditas ekspor di Indonesia. Salah satu jenis ikan hias tawar yang dibudidayakan dan memiliki peluang pasar yang bagus adalah ikan neon tetra (Paracheirodon innesi). Namun demikian, ikan hias khususnya ikan hias air tawar masih diproduksi petani dalam skala kecil.
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Judul: Produksi ikan neon tetra Paracheirodon innesi ukuran L pada padat tebar 20, 40 dan 60 ekor/liter dalam sistem resirkulasi
Abstrak: Salah satu masalah dalam produksi ikan neon tetra Paracheirodon innesi adalah ikan ini mudah men-ga lami stres ketika ter"i adi verubahan kualitas air. sehingga diperlukan kualitas air yang relatif stabil selama pemeliharaan. Resirkulasi merupakan salah satu altematif dalam menciptakan lingkungan budidaya yang -optimal. Peningkatan produksi dapat dihasilkan -denim meningkatkan padat penebaran. Oleh karena itu diperlukan suatu penelitian mengenai padat penebaran dalam sistem resirkulasi yang dapat menghasilkan produksi yang maksimal dengan efisiensi usaha yang tinggi. Penelitian ini bertujuan untuk menentukan pengaruh padat penebaran terhadap produksi ikan neon tetra dan menentukan kepadatan yang optimal.
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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