Judul: Seleksi peubah dengan analisis komponen utama dan procrustes
Abstrak: Principal component analysis (PCA) is a dimension-reducing tool that replaces the variables in a multivariate dataset by a smaller number of derived variables. Dimension reduction is often undertaken to help in interpreting the data set but, as each principal component usually involves all the original variables, interpretation of a PCA can still be difficult. One way to overcome this difficulty is to select a subset of the original variables and use this subset to approximate the data. Procrustes analysis as a measure of similarity, is used to measure the efficiencies of the alternative variable selection methods in extracting representative variables Because of its unavailability in statistical software, a package program, using Mathematica 8.0, is composed for variable selection. There are four variable selection methods, based on PCA and procrustes analysis, which have been described and examined along with different criteria levels for deciding on the number of variables to retain in the analysis. The methods are B2, B4, procrustes analysis in the principal component score, and procrustes analysis method. The result show that variable selection programs B2 as the best variable selection method followed by procrustes analysis method. Moreover, it is found that all of the methods considered give the measure of efficiency of more than 99.04%.
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Judul: Penerapan Analisis Komponen Utama Kekar dengan Metode ROBPCA
Abstrak: Data dengan jumlah variabel dan observasi besar seringkali memerlukan penanganan khusus, baik dalam proses analisis maupun visualisasi. Proses analisis dan visualisasi ini bisa disederhanakan dengan cara reduksi dimensi menggunakan analisis komponen utama (AKU). Keberadaan pencilan akan sangat memengaruhi analisis suatu data sehingga diperlukan analisis dengan suatu pendekatan yang kekar (robust) terhadap pencilan. Salah satu pendekatan yang bisa digunakan adalah AKU kekar ROBPCA yang menggabungkan konsep projection pursuit dengan estimator Minimum Covariance Determinant. Suatu data contoh dianalisis menggunakan AKU klasik dan AKU kekar dengan metode ROBPCA. Berdasarkan plot tebaran score distance dan orthogonal distance, AKU ROBPCA efektif dalam mengidentifikasi pencilan dibandingkan dengan AKU klasik. Lebih jauh, hasil analisis biplot juga menunjukkan AKU ROBPCA lebih akurat dalam penanganan pencilan dibandingkan AKU klasik.
Keyword: kekar, analisis komponen utama, minimum covariance determinant, pencilan, projection pursuit

Judul: Antibody Responses against Newcastle Disease and Evaluation of Infectious Bronchitis Maternal Derived Antibody on Vaccinated Layers.
Abstrak: Information of maternal derived antibody (MDA) and onset of immunity after vaccination is important to determine appropriate vaccination programme of chickens. The aim of this study was to evaluate duration of Infectious Bronchitis (IB) MDA and evaluate onset of immunity Newcastle Disease (ND) vaccination among layers. All layers were vaccinated using live ND and Infectious Bursal Disease (IBD) at one day old age, and received Avian Rhinotracheitis (AR) vaccine at 4 day old age. Serum samples were collected at various ages: 7, 10, 14, 20, 22, 24, 29 and 35 day old. The presence of ND antibodies was measured by the hemagglutination inhibition (HI) test, whilst the presence of IB antibodies was measured by enzyme-linked immunosorbent assay (ELISA). Our study showed that ND antibodies were at positive level until 24 day old age, and reached protective level ≥ 25 only at 7 day old age, whilst IB MDA were positive up to 17 day old age. This result indicating the needs of ND booster vaccination at least at 24 day old and IB vaccination at 17 day old age.
Keyword: antibody, Infectious Bronchitis, layers, Newcastle Disease, vaccination