Judul: Dryopteris irregularis Presl
Abstrak: Keanekaragaman hayati hutan tropika Indonesia merupakan sumber senyawa metabolit sekunder berpotensi yang tak ternilai jumlah dan jenisnya. Tumbuhan pakll Ihnat (Dlyopleris irregularis Pres I) term8suk dalam jenis tumbuhan paku yang penyebaran tumbuhnya luas dan berdasarkan infonnasi elnobotani sering digunakan untuk obat sakit perut, sakit panas, dan penyakit kulit.
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Judul: Dominasi tumbuhan bawah liar yang berperan sebagi tumbuhan obat di Taman Nasional Gunung Gede Pangrango seksi Bogor
Abstrak: Konservasi kawasan alam Bodogol merupakan hutan hujan tropis yang terletak di sebelah barat Gunung Gede Pangrango, Jawa Barat, pada ketinggian kurang lebih 800 meter di atas permukaan Laut (m dpl). Kawasan Bodogol terdapat tiga jalur lintasan, yaitu jalur bendungan (600-700 m dpl), Jalur kanopi (700-800 m dpl) dan jalur air terjun (800 - 900 m dpl). Pada jalur bendungan, tumbuhan bawah liar yang berperan sebagai tumbuhan obat di bawah naungan pohon rasamala terdapat 17 spesies yang didominasi oleh paku rane (Selaginella wildenowii Baker) dengan Indeks Nilai Penting (INP) 114.6 %, sedangkan di bawah naungan pohon puspa terdapat 11 spesies yang didominasi oleh harendong bulu (Clidemia hirta (L.) D.Don) dengan INP 131.1 %. Pada jalur kanopi tumbuhan bawah liar yang berperan sebagai tumbuhan obat di bawah naungan pohon rasamala terdapat 12 spesies didominasi oleh harendong bulu (Clidemia hirtu (L.) D.Don) dengan INP 128.5 %, sedangkan di bawah naungan pohon puspa terdapat 12 spesies didominasi oleh harendong bulu (Clidemia hirta (L.) D.Don) dengan INP 122.2 %. Pada jalur air terjun tumbuhan bawah liar yang berperan sebagai tumbuhan obat di bawah naungan pohon rasamala terdapat 19 spesies yang didominasi oleh paku rane (Selaginella wildenowii Baker) dengan INP 96.7 %, sedangkan di bawah naungan pohon puspa terdapat 21 spesies yang didominasi oleh paku rane (Selaginella wildenowii Baker) dengan INP 84.6%. Berdasarkan hasil analisis vegetasi yang dilakukan pada ketiga jalur tersebut diperoleh inventarisasi tumbuhan bawah liar yang berperan sebagai tumbuhan obat sebanyak 35 jenis. Hasil analisis ekologis secara keseluruhan didapatkan tumbuhan dominan yaitu paku rane (Selaginella wildenowil Baker) dan tumbuhan kodominan yaitu harendong bulu (Clidemia hirta (L.) D.Don). Untuk nilai keanekaragaman jenisnya berdasarkan indeks Shanon (H) sebesar 1,23.
Keyword: Medicinal Plant, Clidemia hirta (L.) D.Don, Clidemia hirta (L.) D.Don

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