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: 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: Growth hormone gene transmission and expression in  the second generation of transgenic betta fish (Betta imbellis)
Abstrak: Betta fish (Betta imbellis) is one of the endemic freshwater animals spread in several regions in Indonesia. B. imbellis fish is an ornamental fish commodity that has the potential to be cultivated. Betta fish fans prefer the larger size (giant) with a body length of > 6 cm than the regular size (approximately 5 cm). Betta fish giant size is still a favorite fish in international contests, and it is still difficult to get a fish that size. Giant-sized betta fish have a high market value, ranging from IDR 900,000 to IDR 950,000 per fish. One alternative method that can be applied to produce a giant betta fish is the transgenic method. Transgenic is a genetic engineering technique that is done by inserting foreign genes into the host genome, these genes are then expressed in certain phenotypes, and passed on to the next generation. Most transgenesis in fish use growth hormone (GH) encoding genes with the aim of increasing growth so that the maintenance time to reach selling size is shorter. Generally, GH transgenesis is also carried out in consumption fish. In ornamental fish, generally transgenic production is designed for color engineering. The G1 transgenic broodstock were identified by polymerase chain reaction (PCR) method, then crossbreeding was carried out by the reciprocal method by crossing transgenic G1 broodstock and non-transgenic fish. DNA was extracted from the fin-clipped tail, then used in the analysis of gene transmission in the second generation (G2) using the PCR method. Total RNA was extracted from pituitary, muscle, liver and fins following the Tri-Reagent Kit procedure for gene expression analysis. Complementary DNA (cDNA) synthesis was carried out using the Transcriptor First Strand cDNA Synthesis Kit. Parameters of reproductive performance (fertilization rate and hatching rate), growth (absolute growth rate, specific growth rate), fish survival, percentage of transgene transmission and gene expression level were statistically analyzed by analysis of variance ANOVA using the SPSS software version 18.0. Further analysis was performed using Duncan's test at a 95% confidence interval. This study showed that fertilization and hatching rates varied among crosses, and survival was the same in all crosses. Transgenic crossbreed fish was more than 90% progenies carrying the PhGH transgene, identified with transgene transmission from G1 parent to G2 ranging from 90.0% to 96.6%. The growth performance of G2 transgenic betta fish was higher than that of non-transgenic fish, which was 1.4-1.9 times. The PhGH gene expression was only detected in transgenic fish, whereas BeGH gene expression was detected in transgenic and non-transgenic fish with the same level of expression. The IGF-1 gene expression level of G2 transgenic betta fish was higher than that of non-transgenic betta fish. The mean length and body weight of transgenic fish were 4.28±0.70 cm and 1.77 ± 0.05 g, while the non-transgenic fish were 3.40±0.20 cm and 1.40±0.04 g, respectively In conclusion, G2 offspring have inherited the percentage of PhGH transgene inherited to G2 progenies with a higher percentage of transgenic fish compared to their G1 generation. The PhGH gene was expressed in all G2 transgenic so that had a higher growth rate compared
Keyword: Betta imbellis, growth hormone (GH), insulin-like growth factor-1, transmission, transgenic