Judul: Kajian Penerapan Sistem Irigasi Curah dan Irigasi Tetes untuk Tanaman Tebu di PT Gunung Madu Plantations Lampung
Abstrak: Tebu merupakan salah satu tanaman penghasil gula yang telah diusahakan dengan teknik budidaya lahan kering yang menghendaki adanya pendayagunaan air secara optimal. Besarnya kebutuhan air untuk pertumbuhan tanaman tebu menentukan banyaknya tambahan air irigasi yang diperlukan, baik melalui teknologi irigasi alur, irigasi curah, maupun irigasi tetes.
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Judul: Analisis kelayakan teknik dan ekonomi pada penerapan sistem irigasi curah tipe big gun di PT.Gunung Madu Plantations, Lampung
Abstrak: Budidaya tanaman tebu di lahan kering yang hanya mengandalkan curah hujan sehingga hujan merupakan sumber utama ketersediaan air bagi tanaman. Jumlah dan distribusi curah hujan sangat mempengaruhi keseimbangan antara ketersediaan air dengan kebutuhan tanaman. Ketersediaan air tanah di daerah perakaran yang berlebihan atau kekurangan akan menimbulkan gangguan fisiologis bagi tanaman. Pemberian air tambahan melalui irigasi pada dasarnya perlu pada tanaman tebu selama periode kurang air berlangsung. Dalam jangkauan lebih luas, pemberian air irigasi pada budidaya tanaman tebu tampaknya makin diperlukan untuk mempertahankan produktifitas lahan dari pengaruh dan ancaman kekeringan. Tujuan penelitian ini adalah untuk mengetahui pengaruh pemberian irigasi (satu kali bersamaan dengan saat tanam) terhadap ketersediaan air tanah di daerah perakaran tanaman, serta mengkaji kelayakan pengoperasian jaringan irigasi curah dan memberikan alternatif penyempurnaan sistem irigasi curah sehingga dapat dicapai efisiensi yang lebih tinggi. Penelitian masalah khusus ini dilakukan di PT. Gunung Madu Plantations, Lampung Tengah. Kegiatan penelitian meliputi pengamatan, pengukuran di lapang dan pencatatan data yang dilakukan pada bulan Juli sampai bulan September 1998. ...
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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