Judul: Teknik Pembibitan Dan Organisme Pengganggu Bibit Durian Menoreh Kuning Di Kecamatan Kalibawang, Kulon Progo.
Abstrak: Durian (Durio zibethinus Murr.) varietas Menoreh Kuning merupakan tanaman buah unggulan Kabupaten Kulon Progo, Yogyakarta. Pengembangan durian tersebut dilakukan secara intensif dan dimulai dengan upaya penyediaan bibit berkualitas. Penelitian ini bertujuan mempelajari teknik pembibitan, dan menginventarisasi keberadaan organisme pengganggu bibit durian Menoreh Kuning di Kecamatan Kalibawang, Kulon Progo. Bibit diperbanyak dengan teknik okulasi menggunakan batang atas durian Menoreh Kuning dan satu atau dua batang bawah. Hama yang ditemukan adalah Allocaridara sp, Xyleborus sp, Coptotermes sp, Tetranychus sp, Atractomorpha sp, dan Valanga sp. Penyakit yang ditemukan adalah bercak daun Corynespora sp, hawar daun Rhizoctonia sp, antraknosa Colletotrichum sp, alga Cephaleuros sp, embun hitam Meliola sp, mati pucuk Phytophthora sp dan layu Phytophthora sp.
Keyword: durian, hama, okulasi, penyakit

Judul: Indentifikasi bibit asal biji dan bibit klonal durian (Duria zibethina Murray) berdasarkan ciri morfologi bagian vegetatif
Abstrak: Penelitian bertujuan mengidentifikasi bibit asal biji durian lokal Bogor varietas Entin, Koclak, Pagar, dan Mentega, mengidentifikasi bibit klonal durian dengan batang atas varietas unggul nasional yang diokulasikan dengan varietas lokal berdasarkan ciri morfologi bagian vegetatif, serta mengetabui pertumbuhan tunas baru setiap varietas yang diokulasikan. Penelitian dilakukan di kebun percobaan IPB, Baranangsiang Bogor. Penelitian menggunakan bibit durian asal biji varietas Entin, Koclak, Pagar, dan Mentega sebagai batang bawab serta Hepe, Monthong, Entin, Koclak, Pagar, dan Mentega sebagai batang atas. Jumlah masing-masing varietas batang bawab yang diokulasi dengan setiap varietas batang atas ialah sebanyak 35 buah bibit. Okulasi yang dilakukan menggunakan metode patch budding. Ciri morfologi yang diamati ialab bentuk mabkota bibit, sudut percabangan, warna batang utama, distribusi cabang, bentuk daun, bentuk ]ekukan ujung daun, bentuk lekukan pangkal daun, urat tulang daun, wama permukaan atas daun, warna permukaan bawab daun, permukaan antar tulang daun, dan lengkung daun. Analisis yang dilakukan adalab penelusuran kehomogenan dengan uji khi-kuadrat, sedangkan pembentukan gerombol varietas dilakukan dengan menggunakan analisis gerombol (cluster analysis) berdasarkan ciri morfologi setiap varietas. Analisis komponen utama (principal component analysis) dilakukan untuk mengetabui ciri yang dapat dijadikan pembeda antar varietas serta menentukan posisi varietas pada diagram pencar. Penentuan komponen utama diwakili oleh keragaman keseluruhan data minimal 75%. Untuk mengetabui pengaruh batang bawab dan batang atas dilakukan analisis ragam faktorial rancangan acak lengkap. Uji lanjut dengan menggunakan analisis Duncan.
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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