Judul: Identifikasi kerusakan dan produktivitas gergaji rantai di PT INHUTANI V Jambi : Eks HPH PT Sadarnila dan HPH PT Loka Rahayu
Abstrak: Pengetahuan tentang pemakaian dan pemeliharaan gergaji rantai mutlak diperlukan bagi setiap operator. Mengetahui komponen-komponen gergaji rantai dan fungsi-fungsinya akan memudahkan pemeliharaannya. Jumlah kerusakan komponen gergaji rantai yang dialami oleh operator berkisar 15 hingga 142 buah, dan kerusakan tertinggi terdapat pada rantai (81 buah). Kesalahan-kesalahan yang sering dilakukan oleh operator dapat menyebabkan kerusakan pada komponen gergaji rantai dan dapat membahayakan keselamatan jiwanya. Waktu rata-rata untuk Mengisi bahan bakar adalah 0,1219 menit, Mencari pohon adalah 1.0920 menit, Pembersihan/kliring area adalah 0,6405 menit, Memotong pohon adalah 2.2307 menit, Memotong pangkal adalah 0,8324 menit, Memotong ujung adalah 0,5665 menit, Pindah dari pangkal ke ujung batang adalah 0,4394 menit, sedangkan Waktu hilang akibat mengikir dan mengencangi rantai serta terjepitnya bilah adalah 1,3457 menit. Jadi rata-rata waktu kerja untuk menebang pohon adalah 7,2692 menit. Volume rata-rata untuk satu buah pohon di areal tersebut adalah 6.6136 m³. 3 dan prestasi kerjanya adalah 61.97 m³/jam. Kebutuhan gergaji rantai di areal HPH tersebut adalah (sebagai asumsi bahwa jumlah hari kerja di HPH adalah 20 hari dengan jumlah jam kerjanya adalah 6 jam) adalah 17 alat. Untuk mengurangi besarnya kerusakan alat perlu dilakukan pemberian informasi mengenai pentingnya perawatan gergaji rantai bagi para opearator....
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Judul: Analisis Kerusakan Komponen dan Produktivitas Gergaji Rantai di Perum Perhutani Divisi Regional Jawa Timur KPH Madiun
Abstrak: The correct usage of chainsaw will increase the logging productivity. In the other hand, the incorrect usage will cause loss such as decline of the productivity, damage to the chainsaw and also causing occupational accident for the operator. The objectives of this research to find out the damage component, the cause of the damage component in chainsaw and also to find out the effect of the damage component towards logging productivity. The data was collected by analyzing the damage component, the cause of the damage chainsaw component and also by analyzing the effect of the damage component towards logging productivity. The analysis of the damage chainsaw component show that the most frequently damage component are chain, spark plug, piston and sprocket. The lack of understanding about the correct maintenance and treatment way for the tools and the usage of non-original part is one of the factors that cause the damage. The damage of chainsaw component can influence the non-effective work hours which result in the size of productivity.
Keyword: Bogor Agricultural University (IPB), productivity, influence, damage, component, chainsaw

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