Judul: Kualitas Pulp Paraserianthes falcataria (L) Nielsen dan Acacia mangium Willd Hasil Pemasakan Polisulfida
Abstrak: Seiring dengan kemajuan ilmu pengetahuan dan teknologi, industri pulp dan kertas berkembang cukup pesat dengan laju pertumbuhan konsumsi kertas 10,2% pertahun (Silitonga 1992). Indonesia memiliki peluang yang besar untuk menjadi negara produsen pulp dan kertas baik untuk memenuhi kebutuhan dalam negeri maupun untuk ekspor.
Keyword: 

Judul: Sifat pulp alcell kulit tanaman saeh(Broussonetia papyrifera Vent.)
Abstrak: Kebutuhan pulp dan kertas dunia meningkat dari tahun ke tahun. Hal ini dapat dilihat dari produksi pulp yang terus meningkat dibeberapa negara seperti Kanada, Jepang, Inggris dan Malaysia. Begitupula di Indonesia yang pada tahun 1999-2000 produksi pulpnya meningkat sebesar 4,74%. Peningkatan produksi pulp ini tentunya diikuti juga dengan peningkatan permintaan bahan baku utama berupa kayu. Untuk mendukung perkembangan industri pulp dan kertas di Indonesia perlu diupayakan untuk mencari bahan baku lain yang menghasilkan pulp dengan kualitas yang diharapkan. Kulit tanaman saeh (Broussonetia papyrifera Vent) merupakan salah satu bahan baku pulp non-kayu alternatif. Kulit tanaman saeh merupakan jenis tanaman yang mempunyai serat panjang berkualitas cukup tinggi.
Keyword: pulp, alcell, kertas

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