Judul: Kadar Malondialdehida Ginjal dan Hati Ayam Broiler yang Di Stunning dan Non-Stunning
Abstrak: Malondialdehida (MDA) merupakan metabolit hasil peroksidasi lipid yang dapat menggambarkan aktivitas radikal bebas di dalam sel sehingga dijadikan sebagai indikator stres oksidatif. Penelitian ini bertujuan menghitung kadar MDA pada hati dan ginjal ayam broiler guna mengetahui tingkat stres pada ternak yang disembelih dengan proses stunning dan non-stunning. Metode stunning yang digunakan pada penelitian ini adalah electric stunning via water bath. Alat stunning yang digunakan adalah alat yang dibuat secara manual yang memiliki kapasitas arus 146 mA sebagai nilai arus standar pada stunning ayam. Sebanyak 20 ekor ayam broiler betina dibagi dalam dua kelompok yaitu stunning dan tanpa stunning. Pengujian kadar MDA dilakukan menggunakan metode thiobarbituric acid reactive subtances (TBARS). Hasil penelitian menunjukkan bahwa kadar MDA ginjal ayam yang di non-stunning lebih tinggi 2.933 ± 0.015 μg/gram sampel dari kadar MDA ginjal ayam yang di stunning 2.256 ± 0.015 μg/gram sampel. Kadar MDA hati ayam yang di non-stunning 1.324 ± 0.105 μg/gram sampel tidak berbeda dengan kadar MDA hati ayam yang di stunning 1.219 ± 0.015 μg/gram sampel. Kadar MDA hati ayam yang di non-stunning dan stunning lebih rendah dari kadar MDA ginjal ayam yang di non-stunning dan stunning. Kesimpulan dari penelitian ini adalah ayam yang di stunning sebelum penyembelihan dengan arus 146 mA diduga tidak menimbulkan stres.
Keyword: ayam broiler, malondialdehida, non-stunning, stunning

Judul: Kadar Malondialdehida (MDA) Jantung dan Limpa Ayam Broiler yang di Stunning dan Non-Stunning
Abstrak: Malondialdehida (MDA) adalah indikator stres oksidatif yang disebabkan oleh aktivitas radikal bebas di dalam sel. Penelitian ini bertujuan untuk menghitung kadar MDA sebagai indikator stres pada ayam yang disembelih dengan proses stunning dan non-stunning. Sebanyak 20 ekor ayam disembelih kemudian organ jantung dan limpanya diambil dalam kantung plastik berisi larutan PBS-KCl pH 7.4 untuk dianalisa MDA. Metode stunning yang digunakan pada penelitian ini adalah electric stunning via waterbath. Alat stunning yang digunakan memiliki kapasitas arus 146 mA sebagai nilai arus standar pada stunning ayam. Pengujian kadar MDA dilakukan menggunakan metode Thiobarbituric Acid Reactive Substances (TBARS). Hasil penelitian menunjukkan bahwa kadar MDA jantung ayam yang di stunning 4.481±1.235 μg/gr sampel lebih tinggi jika dibandingkan dengan kadar MDA di ayam yang non-stunning 2.146 ± 0.356 μg/gr sampel. Kadar MDA limpa ayam yang di stunning 3.021± 0.232 μg/gr sampel juga lebih tinggi jika dibandingkan dengan MDA limpa ayam yang non-stunning 2.412 ± 0.336 μg/gr sampel. Kadar MDA limpa ayam yang di stunning dan non-stunning lebih rendah dari kadar MDA jantung ayam yang di stunning maupun non-stunning. Kesimpulan dari penelitian ini adalah ayam yang di stunning sebelum penyembelihan diduga stres yang dibuktikan dengan peningkatan kadar MDA organ jantung dan limpa.
Keyword: broiler, malondialdehida, pemingsanan, penyembelihan

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