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

Judul: Perbandingan Performa Parameter Ridge-K pada Regresi Ridge untuk Mengatasi Multikolinearitas
Abstrak: Multikolinearitas terjadi ketika antar peubah bebas terdapat hubungan linear. Multikolinearitas dapat membuat hasil pendugaan koefisien regresi menggunakan analisis regresi linear berganda tidak akurat. Salah satu cara mengatasi multikolinearitas adalah regresi ridge. Regresi ridge merupakan modifikasi dari metode kuadrat terkecil dengan penambahan nilai tetapan bias k. Nilai parameter k pada regresi ridge mempunyai peran penting untuk mengontrol bias regresi terhadap rata-rata peubah tak bebas. Penelitian ini menerapkan regresi ridge menggunakan nilai parameter yang diusulkan oleh Hoerl, Lawless dan Wang serta Dorugade pada data simulasi yang memiliki tingkat multikolinearitas rendah, sedang, tinggi, sangat tinggi dan pada data riil yaitu data indeks pembangunan manusia (IPM) di Pulau Jawa. Hasil analisis pada data simulasi menunjukkan bahwa regresi ridge merupakan metode terbaik pada data dengan tingkat multikolinearitas sedang, tinggi, dan sangat tinggi. Penerapan pada data IPM menunjukkan bahwa regresi ridge dengan parameter Hoerl merupakan metode terbaik pada data dengan tingkat multikolinearitas tinggi., Multicollinearity occurs when there is linear relation among independent variables. Multicollinearity can effect the estimation of regression coefficients using multiple linear regression inaccurate. Ridge regression is one of the ways that can be used to overcome multicollinearity. It is a modification of the least-square method with adding the value of constant k. The parameter k on the ridge regression has important roles to control the regression bias to the average of dependent variable. The ridge regression in this research is applied using the parameter that proposed by Hoerl, Lawless and Wang, and also Dorugade on the simulation data that has low, moderate, high, very high multicollinearity levels and also applied on the riil data, Human Development Index (HDI) in Java Island. The result on the simulation data shows that ridge regression is the best way for the data in low, moderate, high, and very high multicollinearity levels. The application on HDI data shows that the ridge regression with Hoerl's parameter is the best method for high multicollinearity data.
Keyword: multicollinearity, ridge parameter, ridge regression

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