Judul: Pengaruh Kadar Protein Ransum Terhadap Penampilan Kera Ekor Panjang (Macaca fascicularis)
Abstrak: Kera ekor panjang (Macaca fmcicularis) adalah satu satwa primata yang dimiliki oleh alam fauna Indonesia. Kera ini sangat potensial dilihat dari segi ekonomis dan jasa. Dari segi ekonomis, usaba penangkaran satwa ini memberikan harapan yang cukup baik, terutama bagi Indonesia yang merupakan salah satu pengekspor kera di dunia. Hal ini ditunjukkan dengan makin meningkatnya jumlah ekspor kera Indonesia dari tahun ke tahun. Dari segi jasa satwa ini digunakan sebagai hewan percobaan (biomedis) dan pengembangan ilmu pengetahuan lainnya.
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Judul: Tiga macam ransum monyet ekor panjang (Macaca fascicularis) dan pengaruhnyaa terhadap performa
Abstrak: Penelitian ini dilakukan di Pusat Studi Satwa Primata LP-IPB dari bulan Maret sampai Juni 1999 yang bertujuan untuk membandingkan ransum impor dengan ransum yang terbuat dengan bahan baku lokal dan untuk memgetahui pengaruhnya terhadap penampilan monyet ekor panjang. Hewan percobaan yang digunakan adalah monyet ekor panjang betina dengan bobot badan berkisar antara 1.9-2.8 kg dengan kisaran umur 4-5 tahun. Rancangan percobaan yang digunakan adalah Rancangan Acak Lengkap (RAL) dengan tiga perlakuan (tiga jenis ransum: ransum impor yang dinyatakan dengan R1 dan dua ransum lokal yang dinyatakan dengan R2 da R3) dengan masing-masing enam kali ulangan. R3 berbeda dengan 2 ransum perlakuan lainnya adalah dalam bentuk tekstur yang relatif segar dan lunak. Data yang diperoleh dianalisis dengan sidik ragam, dan apabila berbeda nyata (P<0,05) diuji dengan Uji Jarak Duncan. Hasil penelitian menunjukkan bahwa ransum lokal II (R3) memberikan pengaruh tertinggi terhadap konsumsi dan pertambahan bobot badan dibandingkan dengan ransum impor (R1) dan ransum lokal I (R2). Biaya ransum R3 ternyata terendah bila dibandingkan dengan R1 atau R2 yaitu masing-masing Rp. 1.282,50, Rp. 1.393,66 dan 1302,60.
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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