Judul: Stndi Tentang Laju Pancing, dan Pola  Musim Penangkapan Tuna Mata Besar (Thunnus obesus) Di Perairan Selatan  Jawa - Sumbawa, Samudera Hindia (Studi Kasus PT. Sari Segara Utama).
Abstrak: Pemilihan Perusahaan Sari Segara Utama yang memiliki pangkalan pendaratan ikan di Pelabuhan Benoa, Bali didasarkan pada letak perusahaan yang strategis yaitu terletak di pelabuhan Benoa, Bali yang merupakan salah satu tempat pendaratan ikan, khususnya ikan tuna yang besar untuk diekspor ke Jepang dan negara - negara Eropa. Selain itu, perusahaan ini termasuk kedalam perusahaan swasta pengekspor tuna yang terbesar di Bali.
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Judul: Suatu Studi Tentang Penangkapan Tuna oleh PT perikanan Samodra Besar, Benoa Bali
Abstrak: Sampai saat i n i usaha penangkapan tuna d i perairan i-n donesia masih memberikan kemungkinan yang baik untuk dikembangkan. Dari potensi tuna dan skipjack di perairan Indonesia yang diperkirakan sekitar 207 000 ton, namun baru t+ % yyan dikelola. Satu unit penangkapan rawai tuna terdiri dari kapal, alat penangkapan dan tenaga kerja. Kapal yang digunakan untuk operasi adalah kapal besi berukuran rata-rata 111 GT, 370 FIP dan crew sebanyak 23 orang.
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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