Judul: Budidaya dan pelestarian buaya air tawar irian (Crocodylus novaeguineae)
Abstrak: Di Indonesia terdapat beberapa spesies buaya diantaranya adalah Crocodylus porosus, Crocodylus novaeguineae, Tomistoma schlegelii dan Crocodylus siamenesis. Crocodylus novaeguineae baru ditemukan tahun 1928 di lrian Jaya dan Papua New Guinea oleh Schmidt. Dengan adanya perdagangan kulit buaya maka masyarakat mulai memburu buaya-buaya alam untuk dijual tanpa memperhatikan kelangsungan hidup spesies tersebut. Tempat hidup buaya-buaya inipun semakin sempit karena diubah jadi perladangan, lalu lintas air dan daerah pemukiman sehingga terjadi penurunan jumlah buaya secara cepat.
Keyword: Crocodylus porosus, Crocodylus novaeguineae, Tomistoma schlegelii, Crocodylus siamenesis

Judul: Studi penetasan dan pertumbuhan telur hatchiling buaya muara (Crocodylus porosus) di penangkaran PT. Ekanindya Karsa Cikande Kabupaten Serang
Abstrak: Buaya muara merupakan salah satu jenis reptil yang saat ini keberadaannya di Indonesia terancam punah. Hewan ini banyak diburu untuk diambil kulitnya sebagai bahan baku berbagai macam aksesoris seperti tas, sepatu, sabuk, dompet, dll. Fungsi penangkaran buaya adalah untuk menekan eksploitasi dari buaya akibat adanya pemanfaatan seperti di atas. Tetapi pihak penangkaran mengalami permasalahn baru berupa peningkatan produktifitas telur buaya. Penelitian ini bertujuan untuk memberikan informasi bagaimana cara menetaskan telur buaya pada penangkaran serta bagaimana menjaga dan merawat hatchling (anak buaya) yang baru menetas sampai mencapai tahap dimana hatchling tersebut mampu bertahan hidup tanpa perlu adanya penanganan khusus.
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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