Judul: Keberhasilan Budidaya Udang Windu (Penaeus Monodon Fab.) dalam Tambak Intensif yang Menggunakan Petak Perlakuan Air
Abstrak: Budidaya udang intensif dengan padat penebaran benur yang tinggi menuntut pemberian pakan yang tinggi. Sebagian pakan yang diberikan tidak dikonsumsi, ditambah dengan buangan metabolisme dan bangkai organisme lain meningkatkan akumulasi bahan organik di tambak. Peningkatan akumulasi bahan organik di dalam tambak sejalan dengan pertumbuhan udang.
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Judul: Pengaruh Padat Penebaran Terhadap Produksi Udang Windu (Penaeus monodon Fab.) Secara Intensif dengan Rasio Luas Petak Palakuan dan Petak Pembesaran yang Berbeda
Abstrak: Meningkatkan padat tebar dalam budidaya intensif merupakan salah satu upaya yang dilakukan untuk memperoleh produksi yang tinggi. Semakin tinggi padat penebaran maka semakin banyak jumlah pakan yang dibutuhkan. Sisa pakan yang tidak dialcan, ditambah dengan sisa hasil metabolisme udang menyebabkan bertambahnya beban bahan organik di tambak. Penguraian bahan organik yang banyak memanfaatkan oksigen dalam air dan hasil penguraian bahan organik dapat menurunkan kualitas air tambak. Kualitas air tambak yang menurun menunjukan lingkungan hidup yaug kurang baik bagi udang menyebabkan kelangsungan hidup dan pertumbuhan menurum sehingga produksi menjadi menurun. Untuk mempertahankan lingkungan hidup yang baik bagi udang maka perlu dilakukan pengolaan air yang baik sehingga diperoleh kualitas ai tambak yang mampu mendukung kehidupan udang di tambak.
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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