Judul: Antibacterial Activity of Curcumin Biotransformed Compound by Endophyitic Microbe from Turmeric.
Abstrak: Turmeric is contains curcumin that known has antibacterial activity. On the other hand turmeric has endophytic microbe that live in their tissues. Endophytic microbe potentially as biotransformation agent of active compound of their hosts. The aim of this research was to obtain pure extract of curcumin biotransformed compound by endophytic microbe from turmeric and to know antibacterial activity of that compound. Curcumin biotransformed compound was extracted from endophytic microbe culture with technical ethyl acetate. That extract then separated using column chromatography with solvents n-hexane, nhexane- ethyl acetate (20:1), n-hexane-ethyl acetate (5:1), n-hexane-ethyl acetate (2:1), chloroform-methanol(10:1), and chloroform-methanol (5:1). And then purified using preparative layer chromatography with eluent chloroform-methanol (10:1). All of extracts were tested their antibacterial activity with diffusion method against Escherichia coli, Staphylococcus aureus, Bacillus subtilis, Salmonella typhi, and Ralstonia solanacearum with 50 mg/mL concentration. Pure extract rendemen was 2.66% with Rf 0.701. Pure curcumin biotransformed compound extract had higher antibacterial activity than crude extract and semipure extract and had doubled antibacterial activity than curcumin. Curcumin biotransformed compound inhibited all of test bacterias and the most sensitive bacteria was Salmonella typhi with 9.0 mm inhibitory zone diameter.
Keyword: 

Judul: Konsentrasi Hambat Minimum dan Toksisitas Senyawa Hasil Biotransformasi Kurkumin oleh Mikrob Endofit Asal Kunyit
Abstrak: Curcumin can be transformed into its derivative compound with the help of endophytic microbes. The biotransformed compound has been known to possess antibacterial activity toward Salmonella typhi and Staphylococcus aureus. This research was conducted to determine the Minimum inhibition concentration (MIC) of the biotransformed compound using agar diffusion method to find out Salmonella typhi's and Staphylococcus aureus's sensitivity against the biotransformed compound. In addition, the toxicity test based on lethal concentration 50 (LC50) with the Brine shrimp lethal test (BSLT) method against Artemia salina was performed as preliminary test in determining the biotransformed compound’s potency in the development of medicines. The MIC value of this compound against Salmonella typhi was 6.25 mg/mL with the clear zone diameter of 2.5 mm and 25 mg/mL against Staphylococcus aureus with the clear zone diameter of 2.5 mm. The LC50 value of the biotransformed compound was 131.272 ppm, higher than curcumin’s LC50, which is 82.153 ppm. This value was obtained by probit analysis using SPSS (Statistical Product and Service Solutions) program.
Keyword: 

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