Judul: Sebaran Kalor Tungku Berbahan Bakar Sekam Padi dan Cangkang Kelapa Sawit Menggunakan Pendekatan Metode Beda Hingga pada Sterilisasi Jamur Tiram Putih dalam Drum
Abstrak: Telah dihasilkan sebaran suhu untuk proses sterilisasi di dalam drum dengan menggunakan bahan bakar sekam padi dan cangkang kelapa sawit. Sterilisasi bertujuan membunuh bakteri dan jamur lain yang tidak diinginkan pada baglog. Sterilisasi dilakukan dengan cara tradisional yaitu mengukus baglog di dalam drum yang disusun 4 baris ke atas, dengan lama pengukusan 6,8, dan 10 jam masing-masing dua ulangan. Melalui persamaan hantaran kalor yang dihitung secara numerik de
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Judul: Tungku sekam sebagai bahan bakar alternatif pada proses sterilisasi media jamur tiram
Abstrak: Sterilisasi media yang telah dikemas dalam bentuk baglog pada budidaya jamur tiram, salah satunya dapat dilakukan dengan teknik mengukus menggunakan drum. Bahan bakar yang biasa digunakan yaitu kayu bakar, batu bara, menggunakan minyak tanah atau LPG. Sekam padi dengan tungku sekam sebagai alatnya merupakan bahan bakar alternatif yang dapat digunakan sebagai bahan bakar pada sterilisasi media. Rata-rata sekam yang digunakan pada proses pengukuan media jamur tiram putih selama 6 jam, 8 jam, dan 10 jam sekitar 53,5 kg, 60,3 kg, dan 64,8 kg. Efisiensi yang didapatkan pada proses pengukusan media jamur tiram dengan menggunakan tungku sekam mencapai 43,35% pada sterilisasi media selama 8 jam. Dari hasil analisis dengan menggunakan metode rancangan acak lengkap didapatkan bahwa perlakuan lamanya sterilisasi berpengaruh nyata terhadap energi panas yang dibutuhkan dan massa jamur yang dihasilkan tetapi tidak berpengaruh nyata terhadap efisiensi bahan bakar yang dihasilkan.
Keyword: Media, Jamur tiram, Tungku sekam, Sterilisasi, Bogor Agricultural University

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