Judul: Optimasi media ganggang penghasil bahan baku biofuel yang berasal dari Danau Situ Gede, Bogor
Abstrak: Ganggang merupakan tumbuhan yang belum mempunyai akar, batang, daun yang sebenarnya, tetapi sudah memiliki klorfofil sehingga bersifat autotrof. Ganggang dapat digunakan sebagai sumber energi alternatif karena dengan perlakuan tertentu ganggang dapat diekstrak dan menghasilkan minyak.
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Judul: Kajian proses produksi biodiesel dari minyak biji bintaro (Cerbera odollam Gaertn) dengan metode transesterifikasi
Abstrak: Penggunaan bahan bakar minyak dari sumber energi fosil setiap harinya mengalami peningkatan, sedangkan pemenuhan kebutuhan bahan bakar minyak tersebut mengalami penurunan. Hal ini dikarenakan bahan bakar minyak dari sumber energi fosil merupakan bahan bakar yang tidak dapat diperbaharui (nonrenewable). Akibat adanya ketidakseimbangan antara persediaan dan permintaan bahan bakar minyak dan kekhawatiran pasokan minyak menurun, maka diperlukan alternatif sumber energi lain yang dapat dijadikan bahan bakar minyak. Biodiesel merupakan salah satu jenis dari sumber energi alternatif yang dapat dijadikan bahan bakar minyak. Salah satu tumbuhan yang berpotensi untuk dikembangkan di Indonesia sebagai biodiesel adalah pohon bintaro (Cerbera odollam Gaertn). Biji dari pohon ini sangat potensial dijadikan biodiesel dikarenakan kandungan minyak dari biji buah ini cukup tinggi yaitu sekitar 43-64%, bukan merupakan tanaman pangan sehingga tidak akan berkompetisi dengan kebutuhan pangan serta tanaman ini sudah banyak dikultivasi, dijadikan tanaman reboisasi dan penghias pada pinggir jalan raya.
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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