Judul: Studi Pembuatan Papan Partikel Binderless Dari Inti Kenaf (Hibiscus cannabinus L.)
Abstrak: Kenaf merupakan tanaman penghasil serat yang memiliki potensi untuk dijadikan bahan baku papn komposit. Selama ini inti kenaf sudah digunakan sebagai bahan baku papan partikel dengan menggunakan berbagai macam perekat sintetis yang menghasilkan emisi formaldehida, seperti urea formaldehida, melamin formaldehida dan phenol formaldehida. Beberapa penyakit yang telah terdeteksi sebagai akibat dari emisi formaldehida yang berlebihan antara lain iritasi mata, penyakit saluran pernafasan bagian atas, gangguan perencanaaan dan sakit kepala. Oleh karena itu dalam penelitian ini, dibuat papn partikel tanpa perekat sintetis (binderless) dan inti kenaf, dimana papan partikel binderless akan menjadi papan yang ramah lingkungan
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Judul: Studi Pembuatan Papan Partikel Binderless Dari Inti Kenaf (Hibiscus cannabinus L.) dengan Autoclave Pretreatment
Abstrak: Kenaf merupakan salah satu tumbuhan berlignoselulosa yang memiliki potensi untuk dijadikan papan partikel. Sebagian besar papan partikel menggunakan perekat formaldehid seperti Urea Formaldehid (UF), Melamin Formaldehid (MF), Phenol Formaldehid (PF) untuk menyatukan partikel-partikelnya. Di dalam penelitian Ali et al (1998) disebutkan bahwa emisi formaldehid dapat menyebabkan beberapa jenis penyakit yaitu gangguan pencernaan, sakit kepala, gangguan pernafasan dan gangguan mata. Konsentrasi lebih dari 50 ppm bagi orang yang mempunyai riwayat penyakit alergi dan dalam kondisi yang lemah bisa menyebabkan kematian. Berdasarkan sifat fisis dan mekanis papan partikel binderless, penggunaan papan yang sesuai adalah sebagai papan interior dan nonstruktural seperti peredam suara dan insulasi suhu.
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Judul: Quantitative Trait Loci Mapping for Trait in Categorical Scale
Abstrak: Genes or regions on chromosome underlying a quantitative trait are called Quantitative Trait Loci (QTL). Characterizing genes controlling quantitative trait on their position in chromosome and their effect on trait is through a process called QTL mapping. In estimating the QTL position and its effect, QTL mapping basically utilize the association between QTL and DNA markers. However, many important traits are obtained in categorical scale, such as resistance from certain disease. From a theoretical point of view, QTL mapping method assuming continuous trait could not be applied to categorical trait. This research was focusing on the assessment of the performance of Maximum Likelihood (ML) and Regression (REG) approach employed in QTL mapping as well as the performance of Lander and Botstein (LB) and Piepho method in determining critical value in testing the existence of QTL for binary and ordinal trait by means of simulation study. The simulation study to evaluate the performance of ML and REG approach was conducted by taking into account several factors that may affecting the performance of both approaches. The factors are: (1) marker density; (2) QTL effect; (3) sample size; (4) shape of phenotypic distribution; (5) number of categories; and (6) number of QTL. Moreover, the simulation study for evaluating LB and Piepho method in determining critical value was conducted by generating distribution of the test statistic under null hypothesis. From simulation study, it was obtained that LB and Piepho method showing similar performance in determining critical value in testing the existence of QTL for binary and ordinal trait. The simulation study also indicating that both methods could be used in determining critical value in QTL mapping analysis for binary trait as well as for ordinal trait if the REG approach is used but not if ML approach is used due to their poor performance. In assessing the performance of ML and REG approach in QTL mapping analysis for binary trait, the two approaches showing comparable performance; whereas for ordinal trait REG approach showing poor performance compared with ML approach in estimating thresholds. As a result, in QTL mapping analysis, ML and REG approach could be used when dealing with binary trait, whereas ML approach is suggested when dealing with ordinal trait.
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