Judul: Potensi rumput mutiara (Hedyotis corymbosa (L) Lam.) sebagai antioksidan alami
Abstrak: Rumput mutiara adalah tumbuhan peneduh. Tumbuhan ini diduga berpotensi sebagai antioks idan alami. Penelitian ini dilakukan untuk membandingkan aktivitas antioksidasi antara tajuk dan akar rumput mutiara yang dianalisis secara in vitro dan aktivitasnya dibandingkan dengan senyawa atokoferol (Vitamin E). Tajuk dan akar rumput mutiara diekstraksi dengan menggunakan etanol 70% secara refluks. Aktivitas antioksidasinya ditentukan dengan metode asam tiobarbiturat (TBA). Metode ini didasarkan pada reaksi spesifik antara TBA dan malondialdehid (MDA) (hasil oksidasi asam linoleat). Daya hambat dari ekstrak tajuk 500 ppm dan 200 ppm, eks trak akar 500 ppm serta a-tokoferol 200 ppm secara berturut-turut adalah sebesar 71,74%; 61,78%; 63,81% dan 77,61%. Dari hasil uji Duncan (a= 0.05) diperoleh bahwa ekstrak tajuk (500 dan 200 ppm) serta ekstrak akar (500 ppm) memiliki daya hambat pembentukan MDA yang tidak berbeda nyata dengan a-tokoferol. Hasil penelitian ini menunjukkan bahwa penambahan ekstrak tajuk dan akar rumput mutiara dapat menghambat laju pembentukan MDA. Semakin tinggi konsentrasi ekstrak maka semakin besar pula aktivitas antioksidasinya. Dari penelitian ini disimpulkan bahwa ekstrak tajuk rumput mutiara memiliki aktivitas antioksidan yang lebih tinggi dibandingkan ekstrak akar rumput mutiara.
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Judul: Potensi antioksidasi bebrapa ekstrak senyawa bahan alam yang berkhasiat sebagai antikanker
Abstrak: This research was conducted to compare antioxidation potency of some extracts from natural materials compound ivhich have anticankr poiency. The natural compounds tested were ex&& of mohkota dewa, d m d k a , sahbung nyawa, temuputih, and tofu whey. Analysis have been done using 200 ppm total an centration for each extract tested. Natural antioxidant vitamin E (a-tocopherol 0 ppm) is used for comparison. The degree of oxidation was measured by $Ilobarbituric acid (TBA) method in vitro.
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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