Judul: Kadar Kalsium, Fosfor, dan Magnesium dalam Darah Sapi Perah Penderita Mastitis Subklinis
Abstrak: Mineral makro (kalsium, magnesium, dan fosfor) memegang peranan penting dalam produksi ternak sapi perah. Kekurangan dan ketidakseimbangan kalsium, magnesium, dan fosfor dapat menghambat produktivitas sapi perah. Penelitian ini bertujuan mengukur kadar Ca, P, dan Mg dalam darah pada sapi perah penderita mastitis subklinis dengan menggunakan uji mastitis IPB-1. Sampel darah dan susu diambil dari 20 ekor sapi perah yang berasal dari Koperasi Peternakan Bandung Selatan (KPBS) Pangalengan. Kadar mineral makro diperiksa di laboratorium komersial. Hasil pemeriksaan menunjukkan bahwa 13 dari 20 ekor sapi (65%) yang diperiksa memiliki kadar kalsium di bawah kadar normal dan sebanyak 7 diantaranya (53.8%) menderita mastitis subklinis. Sapi perah penderita mastitis subklinis cenderung memiliki kadar kalsium dan kadar magnesium lebih rendah dari normal.
Keyword: fosfor, kalsium, magnesium, mastitis subklinis

Judul: Kadar Kalsium Pada Sapi Perah Penderita Mastitis Subklinis Di Pasir Jambu, Ciwidey, Kabupaten Bandung Barat
Abstrak: Kalsium merupakan makro mineral yang berperanan sangat penting dalam metabolisme ternak sapi perah. Kekurangan kalsium yaitu hipokalsemia dapat menjadi salah satu penyebab mastitis. Penelitian ini untuk mengukur kadar kalsium darah pada sapi perah penderita mastitis subklinis. Sampel susu dan darah diambil dari 20 ekor sapi perah yang berasal dari peternakan rakyat Pasir Jambu, Ciwidey, Kabupaten Bandung Barat. Kadar kalsium diperiksa menggunakan metode Atomic Absorption Spectrofotometer (AAS), sedangkan sampel susu diperiksa untuk menentukan status mastitis subklinis berdasarkan jumlah sel somatik, IPB-1 dan keberadaan patogen. Hasil pemeriksaan menunjukkan bahwa 11 dari 20 ekor sapi (55%) menderita mastitis subklinis dan 6 dari 11 ekor tersebut (54.5%) menderita hipokalsemia subklinis. Jumlah sel somatik dan kadar kalsium dari sapi sehat dan sapi penderita mastitis subklinis menunjukkan perbedaan yang signifikan (p<0.05). Kadar kalsium dan jumlah sel somatik mempengaruhi produksi susu sebesar 99% (R2= 0.99). Simpulan yang dapat diambil dari penelitian ini adalah sapi perah penderita mastitis subklinis memiliki kadar kalsium lebih rendah dibandingkan dengan sapi sehat.
Keyword: hipokalsemia subklinis, jumlah sel somatik, kalsium, mastitis subklinis

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