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: Durability of Specific Immunoglobulin (Ig) Y Againts Influenza and Covid-19 in Sunscreen
Abstrak: The benefit of specific immunoglobulin Y (Ig Y) as an active ingredient in personal care products such as sunscreen has not been properly studied. Our study aims to evaluate the reliability of specific IgY against influenza and the COVID-19 virus in a sample sunscreen product. Specific IgY influenza and COVID-19 viruses were inserted in the sample sunscreen at two concentrations of 0.1% and 0.25%. Three treatments were observed: control (C) sunscreen without IgY, treatment 1 (T1) sunscreen with 0.1% IgY, and treatment 2 (T2) sunscreen with 0.25% IgY. Organoleptic observations were carried out to monitor changes in aroma, color, and texture, and sunscreen sampling was done every week for 6 weeks. The durability of IgY was evaluated using an enzyme-linked immunosorbent assay (ELISA) and a neutralization assay. Organoleptic observation from weeks 1 to 6 showed consistency in its aroma, color, and texture. Enzyme-linked immunosorbent assay and neutralization assay (NA) results of the T1 and T2 groups showed negative results except for sample T1 at week 5, which showed a positive result in the Influenza A ELISA. Based on the results, it seems that IgY concentrations of 0.1% or 0.25% were not optimal. However, this negative result might be due to low concentration of the IgY and the presence of beeswax, which inhibits IgY and causes it to dissolve in distilled water, thus preventing it from being detected in the assay.
Keyword: Immunoglobulin Y, Sunscreen, Influenza, COVID-19, Biological Activity