Judul: Audit energi pada pengolahan susu cair menjadi susu bubuk di PT. Ultrindo Intijaya Jakarta
Abstrak: lndustri pengolahan susu merupakan industri pengolahan bahan pangan yang sangat penting bagi peningkatan kualitas sumber daya manusia. Hal ini disebabkan produk olahan susu merupakan bahan pangan bergizi tinggi dengan nutrisi lengkap kebutuhan tuhuh manusia. Diharapkan dengan adanya industri pengolahan susu akan meningkatkan nilai gizi dan ketahanan umur susu sehingga akan mudah dijumpai dipasaran baik bagi konsumen masyarakat perkotaan maupun masyaiakat pedesaan.
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Judul: Audit energi dan penetuan efisisensi energi dalam proses produksi susu evaporasi di PT Ultrindo Intijaya, Jakarta
Abstrak: Sektor industri di Indonesia menempati urutan pertama dalam mengkonsumsi energi nasional, yaitu sebesar 45-48% dari total pemakaian energi nasional dengan peningkatan rata-rata 13.5% setiap tahun. Industri pengolahan susu adalah merupa- kan industri makanan yang padat energi, sehingga untuk mengantisipasi adanya pemborosan dalam sektor ini, tingkat efisiensi dan langkah-langkah penghematan energi sangat diperlukan. Tujuan dari penelitian ini adalah mengetahui kebutuhan energi komersial per- satuan produk dan tingkat efisiensi penggunaan energi pada proses produksi susu bubuk dan susu evaporasi. Dari hasil yang diperoleh ditentukan penghematan energi pada tahapan proses yang terjadi pemborosan dan pemanfaatan energi yang terbuang. Analisis pengolahan data didasarkan pada metode analisis energi Proses dan dibatasi hanya pada energi komersial. Perolehan data meliputi data primer dan data sekunder yang banyak melibatkan proses pindah panas. Perolehan data primer meliputi pengukuran sifat fisik produk, pengamatan spesifikasi peralatan, penguku- ran suhu masuk, suhu keluar dan suhu medium pada tiap tahapan proses untuk perhi- tungan pindah panas yang terjadi, pengukuran lama proses untuk tiap unit produksi dan untuk tiap peralatan yang digunakan, pengukuran pada proses pengeringan, dan pengukuran luasan instalasi pipa. Sedang perolehan data sekunder didapatkan dari pustaka dengan berdasar pada perolehan data primer. ...
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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