Judul: Prospek usaha budidaya ikan nila gift sistem color pada keramba jaring apung di waduk Cirata Kecamatan Cikalong Kulon Kabupaten Cianjur, Provinsi Jawa Barat
Abstrak: Waduk Cirata merupakan salah satu waduk di Jawa Barat yang memanfaatkan potensinya untuk usaha budidaya ikan. Salah satu wilayah di Waduk Cirata yang menjadikan waduk sebagai usaha budidaya ikan nila gift dalam keramba jaring apung yaitu Kecamatan Cikalong Kulon Kabupaten Cianjur. Pemanfaatan potensi sumberdaya waduk ini sudah melebihi kapasitas daya dukung perairan. Hal ini ditandai dengan jumlah keramba jaring apung yang terus meningkat, sehingga menimbulkan permasalahan terutama yang berkaitan dengan lingkungan. Permasalahan tersebut meliputi kondisi perairan yang semakin buruk ditandai dengan tingkat kekeruhan perairan, terjangkitnya penyakit Koi Herpes Virus yang terutama menyerang ikan mas dan terjadinya umbalan setiap musim hujan tiba.
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Judul: Analisis kelayakan usahatani pembesaran dan pemasaran ikan mas (Cyprinus carpio) budidaya keramba jaring apung (Kasus di Kecamatan Mande, Kabupaten Cianjur, Jawa Barat
Abstrak: Usaha perikanan dengan budidaya kolam jaring apung adalah usaha yang memanfaatkan perairan waduk sebagai media untuk melakukan budidaya pembesaran ikan. Pada mulanya, usaha ini diterapkan dengan tujuan meningkatkan kondisi sosial ekonomi masyarakat di sekitar waduk. Namun perkembangan selanjutnya usaha ini telah menunjukkan perkembangan yang pesat. Budidaya jaring apung ini banyak dilakukan di waduk Cirata, waduk Saguling,dan waduk Jatiluhur. Budidaya ikan dalam keramba jaring apung dapat diadopsi dengan baik oleh sekitar waduk sehingga dapat mendatangkan keuntungan bagi penduduk tersebut.
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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