Judul: Profil Kimiawi dari Formulasi Ekstrak Meniran, Kunyit, dan Temulawak Berdasarkan Aktivitas Antioksidan Terbaik
Abstrak: Indonesia is known as a megabiodiversity countries with the title because it has such biodiversity of flora and fauna. Flora or plant widely used as medicine by traditional means, namely phyllanthin, curcuma, and turmeric. Phyllanthin, curcuma, and turmeric extracts are the plant extracts which contains antioxidant activity. This study aimed to evaluate the antioxidant activity of the formulation phyllanthin, turmeric, and curcuma. Phyllanthin, curcuma, and turmeric is extracted using 96% ethanol and extract formulation carried M:K:T ratio of 1:1:1, 1:1:0, 1:0:1, 0:1:1, 1:0 : 0, 0:1:0, and 0:0:1. Extract formulation phyllanthin: curcuma: turmeric (M:K:T) tested the antioxidant activity with DPPH method. Extracts formulation of which have the highest antioxidant activity of the chemical profile was analyzed by TLC and FTIR. Antioxidant activity which have the highest to explained from extract formulation M:K:T = 1:0:0, 1:1:0, dan 1:0:1 by IC50 percentage antioxidant activity are 33.42 ppm, 45.52 ppm, dan 57.65 ppm. TLC chromatogram profiles obtained from formulations M:K:T = 1:1:0, 1:0:1, and 1:0:0 showed the number of spots as much as 10, 8, and 7 spots. FTIR spectrum results showed functional groups of O-H stretching, C-H stretching, C=C stretching and C=C bending in each formulation which have the highest antioxidant activity.
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Judul: Profil Kimiawi Ekstrak Ramuan Kunyit, Temulawak, dan Meniran Berdasarkan Aktivitas Antioksidan
Abstrak: Research of the antioxidants and chemical profile that generated a lot of herbs have been carried out. The aim of this research is to analyze chemical profile of extracts concoction turmeric, curcuma, and meniran based of the best antioxidant activity. Samples were made formula and prepared by maceration with 96% ethanol. Formula used based on the comparison meniran:turmeric:curcuma, that is 1:1:1, 1:1:0, 1:0:1, 0:1:1, 1:0:0, 0:1:0, and 0:0:1. Antioxidant activity was determined by 1,1-diphenyl-2-picrylhidrazyl (DPPH) method. The extracts concoction chemical profiles was determined by spectrophotometry fourier transform infrared (FTIR) and thin layer chromatography (TLC). The extracts concoction turmeric, curcuma, meniran showed high antioxidant activity because they had IC50 less than 200 ppm, except formula 0:0:1. Three extracts having highest antioxidant activity are formula 1:0:0, 1:0:1, and 0:1:0 were 19.9135 ppm, 69.5428 ppm, and 103.3161 ppm respectively. The chemical profiles from those extracts showed different pattern because they have different functional groups (based of FTIR profiles) and different number of separate compounds (based of TLC profiles).
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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