Judul: Penampilan Reproduksi Sapi Perah KUD Pasirjambu Kecamatan Pasirjambu Kabupaten DT II Bandung Propinsi Jawa Barat
Abstrak: Pengembangan peternakan sapi perah di Kecamatan Pasir jambu dimulai sejak Presiden Republik Indonesia memberikan bantuan sebesar 50 ekor sapi perah jenis FN kepada masyarakat Kecamatan Pasirjambu tahun 1980. Perkembangan populasi sampai bulan November 1986 tercatat 3976 ekor induk yang dimiliki oleh 878 peternak. Perkawinan dilakukan dengan inseminasi buatan.
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Judul: Penampilan reproduksi sapi perah : studi kasus di K.U.D. sarwa mukti Kecamatan Cisarua Kabupaten Daerah Tingkat II Bandung Jawa Barat
Abstrak: Pada persusuan tahun terakhir ini dirasakan bahwa masalah khususnya susu sapi semakin memerlukan penanganan yang lebih serius. Pada PELITA V rencana program kenaikan produksi protein asal hewani, dalam hal ini Susu menempati urutan pertama yaitu 12,9 prosen sedangkan daging dan telur masing-masing 6,25 prosen dan 5,2 prosen. Hal ini sejalan dengan daya beli dan pengetahuan masyarakat tentang gizi semakin meningkat. Studi kasus ini dilaksanakan di KUD. Sarwa Mukti Daerah Tingkat II Bandung Jawa Barat dari awal bulan hingga pertengahan bulan November 1989. Tujuannya adalah untuk mengetahui penampilan reproduksi dan tingkat keberhasilan inseminasi buatan pada sapi perah di KUD. Sarwa Mukti. Data primer diperoleh dengan mewawancarai 50 orang peternak responden, sedangkan data sekunder diperoleh dari instansi terkait dengan materi penelitian. Populasi sapi perah di KUD. Sarwa Mukti terdapat peningkatan dari tahun ke tahun. Pada tahun 1982 tercatat 3.989 ekor dan pada tahun 1988 sudah menjadi 8.045 ekor. ...
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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