Judul: Karakteristik Fermentasi Rumen Dan Sintesis Protein Mikroba In Vitro Dengan Penggunaan Murdannia Bracteata Sebagai Hijauan Fungsional.
Abstrak: Murdannia bracteata merupakan tanaman yang memiliki kandungan mineral tinggi. Penelitian sebelumnya menunjukkan bahwa tanaman ini memiliki kandungan nutrien yang setara dengan hijauan lain (Pennisetum purpureum, Panicum maximum, dan Brachiaria humidicola) serta memiliki kandungan mineral yang tergolong tinggi berdasarkan range kandungan mineral rumput pastura di dareah temperate. Berdasarkan hal tersebut tanaman ini berpotensi dijadikan sebagai pakan fungsional untuk ternak ruminansia. Mineral merupakan nutrien yang dibutuhkan mikroba rumen untuk aktivitas dan sintesis protein tubuhnya. Oleh karena itu mineral yang tinggi pada M bracteata diharapkan dapat membantu meningkatkan aktivitas dan sintesis protein mikroba. Informasi mengenai efek kandungan mineral tanaman terhadap sintesis protein mikroba belum banyak diungkapkan. Hal tersebut pada penelitian ini akan dikaji lebih lanjut untuk melihat pengaruh penggunaan M bracteata dalam ransum terhadap karakteristik fermentasi rumen dan sintesis protein mikroba. Penelitian ini bertujuan untuk mengevaluasi efek penggunaan M bracteata dalam ransum terhadap karakteristik fermentasi rumen dan sintesis protein mikroba rumen. Penelitian ini dilakukan secara in vitro menggunakan rancangan acak kelompok (RAK) yang terdiri dari 4 perlakuan dan 4 kelompok. Pengelompokan berdasarkan perbedaan waktu pengambilan cairan rumen. Cairan rumen diperoleh dari sapi bervistula. Perlakuan terdiri dari R1 (50% Rumput Odot + 0% M bracteata + 50% konsentrat), R2 (50% Rumput Odot + 0% M bracteata + 50% konsentrat mengandung MgSO4), R3 (45% Rumput Odot + 5% M bracteata + 50% konsentrat), R4 (40% Rumput Odot + 10% M bracteata + 50% konsentrat). Peubah yang diukur pada penelitian ini adalah karakteristik fermentasi rumen (nilai pH, konsentrasi amonia, konsentrasi dan proporsional VFA, estimasi produksi gas metan, kecernaan bahan kering dan bahan organik), populasi bakteri, populasi protozoa dan sintesis protein mikroba. Hasil penelitian menunjukkan bahwa penggunaan M bracteata 5% dan 10% dalam ransum tidak memberikan pengaruh yang nyata pada karakteristik fermentasi rumen maupun pada populasi dan sintesis protein mikroba. Penggunaan M bracteata sampai 10% di dalam ransum tidak menurunkan aktivitas mikroba rumen dalam membantu produksi asam lemak terbang dan ammonia. Simpulan dari penelitian tersebut bahwa M bracteata dapat digunakan hingga 10% dalam ransum ruminansia. .
Keyword: Murdannia bracteata, sintesis protein mikroba, karakteristik fermentasi rumen

Judul: Analisis Nutrien dan Fitokimia Tanaman Murdannia bracteata serta Pengaruhnya pada Karakteristik Fermentasi Rumen
Abstrak: The aim of this study was to evaluate nutrients and phytochemicals composition of MB and its effect on the characteristics of ruminal fermentation. Nutrients evaluation included proximate, van soest, mineral, chlorophyll, tannin and saponin analysis. In vitro fermentation by using a randomized block design with 4 treatments (Murdannia bracteata (MB), Pennisetum purpureum (PP), Panicum maximum (PM), Brachiaria humidicola (BH)) was applied. Parameters measured were organic and dry matter digestibility (OMD, DMD), amonia and VFA production. The results showed that dry matter content, crude fiber, and NDF of MB was lower but mineral content was higher than other forages. The tannin content of MB was relatively lower than PP but higher than PM and BH. The saponin content of MB was relatively lower than BH, but higher than PP and PM. The total chlorophyll content of MB was 1.0603 mg g-1.OMD and DMD of MB was highest (P<0.01) than other forages. Ammonia concentration and total VFA production of MB was similar (P>0.05) among treatments
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Judul: The Construction of Greedy SVP LLL Algorithm
Abstrak: A lattice is a set of all integer linear combination of a set of linearly independent vectors in ℝ𝑛���. The independent vectors are called bases of lattice. Any lattice can be generated from many bases, and these bases have the same cardinality. In the lattice, the most fundamental and renowned problem is the Shortest Vector Problem (SVP). SVP is a tracking problem of the shortest nonzero vector in a lattice with equivalent bases. In two dimensions, SVP problem has resolved exactly by Gauss’ algorithm. When the lattice dimension is higher than two, A.K. Lenstra, H.W. Lenstra and L. Lovasz gave a LLL algorithm to compute approximation of nonzero shortest vector in the lattice. Reduced bases solution obtained from LLL algorithm still be an approximation and has polynomial running time of arbitrary dimension which large enough. In 1994, Schnoor and Euchner modified this LLL algorithm as a new variant which later was named deep insertion LLL algorithm. This algorithm is a modified version of the LLL algorithm to the terms of the exchange step to improve the accuracy of output of LLL reduced bases and applying it to the subset sum problem. The purpose of this research are: (1) Reconstruct the LLL algorithm and deep insertion LLL algorithm using a geometric approach. (2) Construct a greedy SVP LLL algorithm. (3) Implement the three algorithms in a symbolic programming language, and evaluate them experimentally. A bases ℬ=[𝐛���1,𝐛���2,…,𝐛���𝑛���] in ℝ𝑚��� is called reduced LLL with parameter 𝛿��� if satisfies: (a) |𝜇���𝑗���𝑖���|≤12, for every integer 𝑖���,𝑗��� with 1≤𝑖���<𝑗���<𝑛���. (b)𝛿���‖𝜋���𝑗���(𝐛���𝑗���)‖2≤‖𝜋���𝑗���(𝐛���𝑗���+1)‖2, for 𝑗���=1,2,…,𝑛���−1,where 𝛿��� is a reduced parameter of real numbers with 14<𝛿���<1. The first requirement is the reduced basis 𝛿��� must “nearly orthogonal” and in its computation case, this requirement can be reached out by using the Gram-Schmidt’s orthogonalization. While the second requirement is called exchange step, or used to called as Lovasz condition. By using both of these requirements, then they establish the LLL algorithm. Then, Schnoor and Euchner modify the LLL algorithm based on the requirements of the exchange step called the deep insertion LLL algorithm, where the exchange step is based on the comparison of the projected vector in orthogonal complement [𝐛���1,𝐛���2,…,𝐛���𝑘���−1]⊥ after j-th reduction. To construct greedy SVP LLL algorithm, the requirements of the exchange step is based on purely by comparing norm of lattice 𝐛���𝑗��� with norm lattice 𝐛���𝑖��� for 𝑖���=1,2,3…,𝑗���−1. The process of insertion reduced vector after the exchange step also has done greedily. Then, we calculate the number of arithmetic operation in greedy SVP LLL algorithm for further analysis. The result of our exprimentation for three algorithms on the certain matrix size shows that the running time of the three algorithms increases propotional to the size of the matrix. The result also shows that by using 𝛿���=34 for the LLL algorithm and the deep insertion LLL algorithm, and the greedy SVP LLL algorithm which is a new variant made by using no parameter of 𝛿���, outperform of the other of two previous algorithm in terms of speed with the same output.
Keyword: LLL algorithm, LLL deep insertion algorithm, greedy SVP LLL algorithm