Judul: Potensi Jarak Pagar (Jatropha curcas Linn) sebagai Sekat Bakar ditinjau dari Kondisi Lebar Tajuk
Abstrak: Kebakaran hutan dapat menyebabkan hutan kehilangan fungsinya sehingga dapat mengganggu keseimbangan ekosistem. Oleh karena itu diperlukan tindakan pencegahan kebakaran hutan. Jarak pagar (Jatropha curcas Linn.) tidak hanya dijadikan sebagai biofuel tetapi dapat juga berpotensi untuk dijadikan sebagai sekat bakar. Penelitian ini bertujuan untuk mengkaji potensi jarak pagar sebagai sekat bakar hijau. Jarak pagar yang diamati berlokasi di Gunung Hambalang seluas 30 m x 30 m, yang terdiri dari 4 kondisi lahan yaitu tepi miring, tepi datar, miring isi, datar isi, masing-masing kondisi terdiri dari 40 tanaman. Dari hasil pengamatan diketahui bahwa jarak pagar dengan pertumbuhan tajuk terbesar adalah pada miring isi dengan nilai rata-rata sebesar 2.191,6 cm2. Namun demikian, jarak pagar pada penelitian ini tidak dapat dijadikan sekat bakar hijau karena pertumbuhannya kurang baik, sehingga tajuk tidak rimbun.
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Judul: Potensi Jarak Pagar (Jatropha curcas Linn) sebagai Jalur Hijau ditinjau dari Laju Dekomposisi Serasahnya.
Abstrak: Salah satu tindakan pencegahan kebakaran hutan adalah dengan pembuatan jalur hijau. Jarak pagar merupakan vegetasi yang direkomendasikan sebagai tanaman jalur hijau. Salah satu syarat vegetasi yang sesuai dijadikan jalur hijau adalah serasahnya cepat terdekomposisi. Untuk mengetahui potensi Jarak pagar sebagai sekat bakar diperlukan penelitian mengenai persyaratan lain vegetasi yang sesuai dijadikan jalur hijau, dalam hal ini laju dekomposisi serasahnya. Sebagai pembanding digunakan tanaman Mahoni (Swietenia macrophylla King). Metode pendugaan laju dekomposisi dalam penelitian ini adalah dengan menghitung penurunan bobot keringnya. Kedua jenis tanaman ini mengalami laju dekomposisi tertinggi pada waktu awal pegamatan. Semakin lama laju dekomposisi semakin menurun. Rata-rata laju dekomposisi Jarak pagar adalah 14.38 % per minggu sedangkan rata-rata laju dekomposisi Mahoni adalah 9.70 % per minggu. Waktu dan jenis mempengaruhi laju dekomposisi. Berdasarkan laju dekomposisi serasahnya Jarak pagar merupakan vegetasi yang sesuai dijadikan tanaman jalur hijau
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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