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: Principal Component Analysis for Outlier Data Using Projection Pursuit and Robust Minimum Covariance Determinant Estimator
Abstrak: Pengelolaan data yang memiliki jumlah peubah dan observasi yang besar tidak dapat dilakukan dengan cara yang biasa. Data yang rumit dan besar seringkali memerlukan pengolahan data yang khusus, baik dalam tahap analisis maupun visualisasi. Analisis Komponen Utama (AKU) merupakan teknik analisis statistika yang mampu mereduksi data dan mentransformasi peubah-peubah asli yang masih saling berkorelasi satu dengan yang lain menjadi satu set peubah baru yang kecil dan tidak berkorelasi satu sama lain. Pencilan (outliers) akan sangat mempengaruhi analisis data sehingga diperlukan analisis dengan suatu pendekatan yang kekar (robust) dengan pencilan. Model yang digunakan untuk data dengan pencilan adalah AKU kekar ROBPCA yang menggabungkan konsep projection pursuit dan estimator Minimum Covariance Determinant. Simulasi dilakukan dengan menggunakan data tanpa pencilan, data dengan proporsi pencilan 5% dan data dengan 10% pencilan. Berdasarkan ukuran kesesuaian dalam menganalisis komponen utama, AKU kekar ROBPCA lebih baik dibandingkan AKU Klasik. Selain itu berdasarkan plot tebaran score distance dan orthogonal distance, AKU kekar ROBPCA lebih efektif dalam mendeteksi pencilan dibandingkan dengan AKU Klasik., Processing data that has a large number of variables and observations can not be managed in the usual way. Complex and large data often require special data processing, both in the analysis and visualization processes. Principal Component Analysis (PCA) is a statistical analysis technique that can reduce data and transform the original variables that are still correlated with each other into a new set of small and uncorrelated variables. Outliers will significantly affect data analysis, so analysis with a robust approach towards outliers is needed. The model used for data with outliers is the ROBPCA which combines the concepts of projection pursuit and the Minimum Covariance Determinant estimator. The simulation was carried out using data without outliers, data with 5% outlier proportions, and data with 10% outliers. Based on the suitability measure in analyzing the principal components, ROBPCA is better than the classical PCA. In addition, based on the scatter plot of the distance score and orthogonal distance, ROBPCA is more effective in detecting outliers than the classical PCA.
Keyword: principal component analysis, classical PCA, ROBPCA, outliers, projection pursuit, minimum covariance determinant

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