Judul: Lalat Penghisap darah (Haematobia exigua de Meijere, 1903) pada  Sapi Sumba Ongole dan Musuh Alaminya.
Abstrak: Penelitian ini bertujuan untuk mengetahui jenis lalat yang dominan penghisap darah pada peternakan sapi Sumba Ongole dm mencari solusi dalam pengendaliannya, terutama pengendalian yang berhubungan dengan musuh alami lalat tersebut.
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Judul: Identifikasi Lalat Sumba (Hippobosca sp.) pada Sapi Perah di Kawasan Usaha Peternakan Sapi Perah Cibungbulang Kabupaten Bogor
Abstrak: This research was aimed to identify and to understand the aspect of bioecology of Sumba flies (Hippobosca sp.) at dairy cows in Dairy Farming Area Cibungbulang Bogor District. The research was conducted from May 2011 to April 2012 and performed in three steps, collecting the of ectoparasites, processing specimens, and identification of ectoparasites. The ectoparasites collected were processed by pinning method. Then ectoparasites were identify by using the key method of Soulsby (1982). The result showed that the sumba flies species infested in dairy cows was Hippobosca equina. The pupae of this fly was put in the corner or crevices of the iron pole in the cowshed. Actually H. equina was the species of dry and hot areas, however, its can adapt and breed in wet and humid areas such as Cibungbulang Bogor District.
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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