Judul: Seleksi Khamir Lokal Penghasil Selenium Organik Asal Feses Sapi untuk Imbuhan Pakan Ternak.
Abstrak: Selenium (Se) merupakan mikromineral esensial, Se berperan dalam melindungi tubuh ternak dari paparan radikal bebas. Pemberian Se-organik dapat meningkatkan ketersediaan mineral dalam tubuh ternak sehingga daya serapnya tinggi. Khamir merupakan mikroorganisme yang berperan dalam menyerap dan mengakumulasi Selenium. Pemberian mineral dalam bentuk organik lebih efektif dibandingkan mineral anorganik sehingga dapat dilakukan penggantian pada mineral anorganik konvensional. Penelitian ini bertujuan untuk mengidentifikasi dan menyeleksi khamir dari isolat lokal yang berasal dari feses sapi sebagai penghasil selenium organik. Isolat dari kotoran sapi diseleksi dan diidentifikasi dengan uji mikroskopis, makroskopis, dan biokimia untuk menentukan genus isolat. Didapatkan empat isolat dengan kode FS 1, FS 3, FS 5, dan FS 6 dengan dugaan kuat isolat khamir berturut-turut yaitu genus Saccharomyces, Hansenula, Torulaspora, dan Candida. Isolat terpilih diberi perlakuan dengan penambahan natrium selenit (Na2SeO3) sebanyak 30 ppm, 45 ppm, dan 60 ppm yang kemudian dihitung menggunakan perhitungan Total Plate Counting (TPC). Isolat dengan nilai pertumbuhan terbaik yaitu FS 1 dengan jumlah koloni 1,53 x 1011 cfu mL-1 pada media tumbuh dengan konsentrasi Se 30 ppm. Khamir FS1 merupakan genus Saccharomyces yang dihitung kandungan Se-nya menggunakan metode GF-AAS yaitu sebesar 0,975 mg L-1. FS1 mampu tumbuh baik pada media yang mengandung Se, Cu dan Zn hingga konsentrasi 60 ppm.
Keyword: cow feces, isolation, local yeast selenium organic

Judul: Selection of Local Yeasts Producing Organic Selenium from Cow Faeces for Feed Additives
Abstrak: Selenium (Se) merupakan mikromineral esensial, Se berperan dalam melindungi tubuh ternak dari paparan radikal bebas. Pemberian Se-organik dapat meningkatkan ketersediaan mineral dalam tubuh ternak sehingga daya serapnya tinggi. Khamir merupakan mikroorganisme yang berperan dalam menyerap dan mengakumulasi Selenium. Pemberian mineral dalam bentuk organik lebih efektif dibandingkan mineral anorganik sehingga dapat dilakukan penggantian pada mineral anorganik konvensional. Penelitian ini bertujuan untuk mengidentifikasi dan menyeleksi khamir dari isolat lokal yang berasal dari feses sapi sebagai penghasil selenium organik. Isolat dari kotoran sapi diseleksi dan diidentifikasi dengan uji mikroskopis, makroskopis, dan biokimia untuk menentukan genus isolat. Didapatkan empat isolat dengan kode FS 1, FS 3, FS 5, dan FS 6 dengan dugaan kuat isolat khamir berturut-turut yaitu genus Saccharomyces, Hansenula, Torulaspora, dan Candida. Isolat terpilih diberi perlakuan dengan penambahan natrium selenit (Na2SeO3) sebanyak 30 ppm, 45 ppm, dan 60 ppm yang kemudian dihitung menggunakan perhitungan Total Plate Counting (TPC). Isolat dengan nilai pertumbuhan terbaik yaitu FS 1 dengan jumlah koloni 1,53 x 1011 cfu mL -1 pada media tumbuh dengan konsentrasi Se 30 ppm. Khamir FS1 merupakan genus Saccharomyces yang dihitung kandungan Se-nya menggunakan metode GF-AAS yaitu sebesar 0,975 mg L-1 . FS1 mampu tumbuh baik pada media yang mengandung Se, Cu dan Zn hingga konsentrasi 60 ppm., Selenium (Se) is an essential micromineral, Se has a role in protecting the body of livestock from exposure to free radicals. Provision of Se-organic can increase the availability of minerals in the body of livestock so that absorption is high. Yeast is a microorganism that has a role in absorbing and accumulating Se. The provision of minerals is more effective than minerals inorganic so that it can be carried out on conventional inorganic minerals. This study aims to identify and select yeast from local isolates in the form of cow feces as the best producer of organic selenium. Isolates from cow feces were selected and identified by microscopic, macroscopic, and biochemical tests to determine the genus. Four isolates were obtained with codes FS 1, FS 3, FS 5, and FS 6 with a strong suspicion of yeast isolates consecutively, namely the genus Saccharomyces, Hansenula, Torulaspora, and Candida. The selected isolates were treated with the addition of sodium selenite (Na2SeO3) as much as 30 ppm, 45 ppm, and 60 ppm which were then counted using Total Plate Counting (TPC) calculations. The isolate with the best growth value was FS 1 with the number of colonies 1.53 x 1011 cfu mL-1 on growth media with a concentration of Se 30 ppm. FS1 is a genus of Saccharomyces whose Se content was calculated using the GF-AAS method, which was 0.975 mg L-1 . FS1 was able to grow well on media containing Se, Cu and Zn up to a concentration of 60 ppm.
Keyword: cow feces, isolation, local yeast, selenium organic

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