Judul: Studi pustaka hama sengon (Paraserianthes falcataria (L) Nielsen)
Abstrak: Pohon sengon (Paraserianthes falcataria (L) Nielsen) tergolong jenis pohon cepat tumbuh (fast growing species) sehingga dapat dipanen dalam waktu 5 – 8 tahun. Pengusahaan hutan sengon, baik berupa hutan milik atau hutan negara, tidak terlepas dari gangguan berbagai jenis hama. Karena itu diperlukan pengetahuan mengenai hama-hama tersebut. Pengentahuan tersebut dapat diperoleh melalui penelusuran pustaka (studi pustaka). Studi pustaka tentang hama sengon ini dilakukan di berbagai perpustakaan selama tiga bulan, mulai dari Mei - Juli 2010. Sumber-sumber pustaka yang dipelajari di tiap perpustakaan di Bogor, Jakarta, Bandung, Yogyakarta, Solo dan Malang, berupa majalah ilmiah, jurnal, laporan penelitian, skripsi, tesis dan desertasi tentang hama sengon.
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Judul: Kecenderungan Produksi perusahaan-perusahaan pembudidaya Sengon (Paraserianthes falcataria (L) Nielsen) Sepuluh Tahun yang Akan Datang
Abstrak: Sengon (Paraserianthes falcataria (L) Nielsen) merupakan salah satu jenis kayu yang cukup besar dibudidayakan di Indonesia, hal ini terjadi karena karakteristik tanaman sengon yang merupakan jenis fast growing stock (tanaman cepat tumbuh) sehingga dapat dipanen pada umur 4- 6 tahun. Tanaman ini menjadi semakin terkenal dikarenakan adanya program “sengonisasi” yang dilakukan pemerintah mulai tahun 1989.
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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