Judul: Ekstrak Etanol Rumput Mutiara (Hedyotis corymbosa (L.) Lam.) Sebagai Antihepatotoksik Pada Tikus Putih yang Diinduksi Parasetamol
Abstrak: Rumput mutiara merupakan rumput liar yang digunakan sebagai obat tradisional dan diduga berpotensi sebagai antihepatotoksik. Penelitian ini dilakukan untuk membuktikan potensi antihepatotoksik tanaman rumput mutiara. Tanaman rumput mutiara diekstraksi dengan pelarut etanol 70%. Hasil ekstraksi diperoleh nilai rendemen sebesar 30.06%. Ekstrak kasar rumput mutiara diidentifikasi dengan uji fitokimia, mempunyai senyawa metabolit sekunder di antaranya alkaloid, flavonoid, saponin, tanin, dan steroid. Ekstrak kasar rumput mutiara tersebut diujikan pada hewan coba tikus putih galur Spraque-Dawley yang menderita gangguan fungsi hati. Pemberian ekstrak etanol rumput mutiara dosis 400 dan 800 mg/kgBB terhadap penurunan kadar SGPT dan SGOT tidak berbeda nyata (P < 0.05) baik pada kelompok yang dihentikan pemberian parasetamolnya pada minggu keempat maupun yang terus dipapar parasetamol. Dosis 400 mg/kgBB ekstrak etanol 70% rumput mutiara sudah mampu menurunkan kadar SGPT dan SGOT tikus setingkat hepatitis kronis pada manusia, yaitu kadar SGPT dan SGOT 4-5 kali dari keadaan normalnya.
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Judul: Khasiat Hepatoproteksi Ekstrak Daun Sangitan (Sambucus Javanica Reinw. ex Blume) pada Tikus Putih Salur Sprague Dawley yang Diberi Parasetamol
Abstrak: Daun sangitan mempunyai aktivitas hepatoprotektor terhadap kerusakan hati yang disebabkan agen hepatotoksik, seperti paracetamol Penelitian ini bertujuan menguji ekstrak daun sangitan terhadap peningkatan aktivitas alanin aminotransferase (ALT) dan Aspartat aminotransferase (AST) serum darah tikus putih yang dicekoki parasetamol dosis tinggi.
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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