Judul: Toksisitas dan Profil Kimiawi Ekstrak Ramuan Meniran, Kunyit, dan Temulawak
Abstrak: Turmeric, curcumin, and phyllantin are one of medicinal plants that has high potency to be explored nowadays. This research was done to determine the toxicity effects from turmeric, curcumin, and phyllantin mixed extract, and also to define the mixed extract chemical’s profile with KLT and FTIR. Mixed extract were made by mixing turmeric, curcumin, and phyllantin in seven different composition, that were 1:1:1; 1:1:0; 1:0:1; 0:1:1; 1:0:0; 0:1:0; 0:0:1. Then its toxicity effect were measured by exposing Artemia salina to the extract for 24 hours. Three best extract with the lowest LC50 were taken to define their chemical’s profile using TLC and FTIR. The best three extract were tumeric: curcumin: phyllantin mixture extract in 1:1:1; 1:1:0; and 1:0:1 proportion, with LC50 5.5377 ppm, 16.3763 ppm, and 11.6243 ppm, respectively. Mixed compound extract with high biological potential (5.537 ppm) show TLC profile with thick spot and without C≡C bond for FTIR spectrum as the other two exctract have thinner spot and have C≡C bond.
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Judul: Profil kimiawi dari formula ekstrak meniran, kunyit, dan temulawak berdasarkan toksisitas terbaik
Abstrak: Phyllantin, turmeric and curcuma that known have antioxidant, antitumor, anticancer, and antibacterial activity. They are commonly used as single extract. Mixing two components of medicinal plant extracts can increase the bioactivity. The aim of this research was to know toxicity of phyllantin, turmeric and curcuma through Brine Shrimp Lethality Test and to know the profiles of TLC and FTIR that formula. Phyllantin, turmeric and curcuma was extracted with ethanol. Extract was made into seven formulation with 1:1:1; 1:1:0; 1:0:1; 0:1:1; 1:0:0; 0:1:0; 0:0:1 compilation. Toxicity of formulation was known through Brine Shrimp Lethality Test. Three best extract with the lowest LC50 were taken to define their chemical’s profile using TLC and FTIR. The best three extract were phyllantin:turmeric:curcuma in 0:0:1, 0:1:0, and 0:1:1, with LC50 17.94567, 20.09533, 37.66333 ppm, respectively. Phyllantin:turmeric:curcuma in 0:0:1 show TLC profile with thinner spot and without C=O bond for FTIR spectrum as the other two exctract have thick spot and have C=O bond.
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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