Judul: Aktivitas antibakteri filtrat Bunga Teleng (Clitoria ternatea L.) terhadap bakteri penyebab konjungtivitis.
Abstrak: Teleng merupakan tanaman polong multiguna karena selain untuk hiasan tanaman ini mengandung senyawa bioaktif yang berguna untuk pengobatan. Akar teleng diduga berkhasiat sebagai tonikum otak yang sangat baik dan bunga teleng berguna untuk mengatasi infeksi mata dan tenggorokkan, penyakit kulit, gangguan urinaria, ulcer dan keperluan anti racun. Penelitian ini bertujuan menguji aktivitas antibakteri filtrat mahkota bunga teleng terhadap bakteri uji: Staphylococcus aureus, Pseudomonas aeruginosa, Escherichia coli, dan Bacillus substilis. Prosedur penelitian ini adalah metode sumur agar, penentuan kadar hambat tumbuh minimal, dan uji koefisien fenol. Hasil penelitian menunjukkan bahwa filtrat bunga teleng memiliki aktivitas hambat tumbuh terhadap empat bakteri uji yaitu: Staph. aureus, P. aeruginosa, B. substilis dan E. coli. Konsentrasi hambat tumbuh minimal sebesar 50 mg/mL untuk menghambat bakteri B. substilis, E. coli, dan P. aeruginosa dan 125 mg/mL untuk menghambat bakteri Staph. aureus. Uji koefisien fenol menunjukkan bahwa filtrat bunga teleng tidak memiliki aktivitas antiseptik terhadap Staph. aureus.
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Judul: Potensi Ekstrak Bunga Telang (Clitoria ternatea) sebagai Antioksidan dan Inhibitor Tirosinase
Abstrak: Clitoria ternatea yang dikenal dengan nama bunga telang adalah tanaman yang banyak digunakan dalam bidang kesehatan seperti antidiabetes, antiinflamasi, antimikroba, antikanker, analgesik, antipiretik dan mengandung kandungan metabolit sekunder yang berperan aktif sebagai antioksidan. Tujuan penelitian ini adalah untuk menguji aktivitas antioksidan ekstrak bunga telang dan inhibitor tirosinase serta mengidentifikasi kandungan fitokimianya. Aktivitas antioksidan ditentukan dengan metode DPPH (2,2-difenil-1-pikrihidrasil). Hasil penelitian ini menunjukkan bahwa, ekstrak bunga telang memiliki aktivitas antioksidan yang sangat lemah dengan nilai IC50 (Inhibition Concentration 50%) lebih dari 200 ppm. Hasil dari aktivitas inhibitor tirosinase menunjukkan bahwa ekstrak bunga telang tidak aktif dalam menghambat enzim tirosinase. Berdasarkan pengujian penapisan fitokimia, komponen kimia yang terkandung dalam ekstrak bunga telang adalah golongan flavonoid, tanin, saponin dan steroid.
Keyword: antioksidan, Clitoria ternatea, DPPH, fitokimia, tirosinase

Judul: Mixed effect models for high –dimensional longitudinal data with latent variables
Abstrak: A set of data that consist of repeated measurements on a large number of outcomes and covariates are known as high-dimensional longitudinal data. The high-dimensional longitudinal data may consist of several effects of interest that cannot be directly measured, known as latent factors or latent variables. During the last decade, the multivariate responses or high-dimensional in longitudinal data have been a big issue. The analysis of high-dimensional longitudinal data is complicated due to its complex correlation structures between outcomes. To understand changes over time of outcome variables, having correlations in individuals with explanatory variables is not enough. Hence, complex correlation structures between outcomes need to be considered. One approach that is commonly used to overcome high-dimensional longitudinal data is simultaneous equation modeling. A well-known simultaneous equation modeling method is the structural equation model (SEM). This approach has several appealing modeling abilities and can be used for high-dimensional longitudinal data. Under the SEM framework, the continuous time SEM is developed to avoid some issues associated with autoregressive and cross-lagged problems in SEM. Another simultaneous equation modeling method is by combining factor analysis and multivariate analysis methods to overcome high dimensional longitudinal data, namely latent factor linear mixed model (LFLMM). The factor analysis is used to reduce the high-dimensional outcomes, and the multivariate linear mixed model is used to study the longitudinal trends of several latent factors. One example of high-dimensional longitudinal data is the General Election Study. This study is carried out repeatedly to observe tendencies towards political attitudes and behavior over time in Belgium. The data contain political information, knowledge, perceptions, and preferences of a political party and the level of participation in politics. One of the most interesting things to study from the data is to analyze the change of political attitudes and behavior of respondents over time. Also, the relationship of changes in these outcomes is important to analyze. The General Election Study in Belgium was designed to include a representative sample of the target population under the Belgian electorate, so accurate estimates about the population could be made. This sampling design was created by the Institute for Social and Political Opinion (ISPO) and the Inter-university Center for Political Opinion Research (PIOP). The Belgian data set contained three subsamples, the Flemish (Dutch-speaking), the Walloon (French-speaking), and the Brussels Capital Region (Dutch and French-speaking). Several studies conducted on the Belgium data are carried out to understand the relationships between the latent variables Individualism (I), Ethnocentrism (E), and Authoritarianism (A) in Flanders. Cross-sectional or a longitudinal studies have also been carried out. In such cases, it is critical to capture the trend of the latent variables over time and, more importantly, whether there is any association or relationships between the development of nationalism (N), ethnocentrism (E), individualism (I), and authoritarianism (A) in Belgium. An empirical analysis of CT-SEM has been done to present the interdependencies among the four latent variables mentioned above on the basis of the General Election Studies for Belgium in 1991, 1995, and 1999 (Interuniversitair steunpunt politieke-opinieonderzoek, 1991, 1995, 1999). Although the four variables have been the subject of
Keyword: Belgium, CT-SEM, high-dimensional longitudinal data, LFLMM, Supplemented EM Algorithm