Judul: Karakteristik Fermentasi dan Mikroba Rumen serta Metabolit Darah Pedet Sapi Bali yang Diberi Pakan Sabun Kalsium Minyak Kedelai.
Abstrak: Penelitian ini bertujuan untuk mengevaluasi karakteristik fermentasi dan populasi mikroba rumen serta metabolit darah pedet sapi Bali yang diberi pakan mengandung sabun kalsium minyak kedelai. Delapan belas pedet dibagi ke dalam 3 perlakuan dan 6 ulangan yaitu P0 : 70% hijauan + 30% konsentrat, P1 : 70% hijauan + 25% konsentrat + 5% sabun ca minyak kedelai, P2 : 70% hijauan + 20% konsentrat + 10% sabun ca minyak kedelai. Hasil penelitian menunjukkan bahwa pemberian sabun kalsium minyak kedelai sampai taraf 10% dalam konsentrat tidak nyata (P>0.05) mempengaruhi pH rumen, VFA total, produksi metan, populasi bakteri total rumen, albumin, protein total, dan glukosa tetapi nyata (P<0.05) mempengaruhi konsentrasi amonia, asetat, propionat, butirat, dan valerat, rasio asetat: propionat, serta populasi protozoa rumen, kolesterol dan trigliserida. Suplementasi minyak kedelai terproteksi level 5% sudah mampu meningkatkan konsentrasi amonia, asetat, rasio asetat:propionat sedangkan pada level 10% menurunkan jumlah populasi protozoa, butirat, valerat, kolesterol, dan peningkatan propionat dan trigliserida. Dapat disimpulkan bahwa suplementasi sabun kalsium minyak kedelai memberikan efek positif terhadap karakteristik fermentasi rumen dan menurunkan kolesterol darah.
Keyword: Pedet sapi Bali, fermentasi, mikroba rumen, metabolit darah, sabun kalsium minyak kedelai

Judul: Performa dan Profil Darah Pedet Sapi Bali yang diberi Ransum Mengandung Sabun Kalsium Minyak Kedelai
Abstrak: Sabun kalsium minyak kedelai (SCa-minyak kedelai) berpotensi diterapkan sebagai suplemen pada pedet sapi Bali agar mendapatkan pedet berkualitas baik dan siap menjadi sapi bakalan. Tujuan penelitian ini untuk menganalisa performa dan profil darah pedet sapi Bali dengan penambahan sabun kalsium minyak kedelai pada ransum. Ternak yang digunakan adalah pedet sapi Bali sebanyak 18 ekor dengan bobot badan awal 131.89 ± 26.02 kg dan dibagi menjadi 3 perlakuan dan 6 ulangan yaitu P0: 70% hijauan + 30% konsentrat, P1: 70% hijauan + 25% konsentrat + 5% SCa-minyak kedelai, P2: 70% hijauan + 20% konsentrat + 10% SCa-minyak kedelai. Hasil penelitian menunjukkan bahwa pemberian SCa-minyak kedelai pada taraf 5% dan 10% nyata meningkatkan (P<0.05) konsumsi konsentrat. Namun demikian, pemberian sabun kalsium minyak kedelai sampai taraf 10% tidak berpengaruh nyata terhadap pertambahan bobot badan, efisiensi pakan, dan Income over feed cost (IOFC) serta profil darah. Dapat disimpulkan bahwa pemberian sabun kalsium minyak kedelai tidak mengganggu kesehatan dan performa pedet sapi Bali.
Keyword: pedet, sapi bali, sabun kalsium, minyak kedelai, profil darah

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