Judul: Daun Pelindung Bunga Temu Lawak (Curcuma xanthorriza Roxb.) sebagai Inhibitor Tirosinase dan Antioksidan
Abstrak: The utilization a bract flower of temu lawak (C. xanthorrhiza) as tyrosinase inhibitor and antioxidant was verified by identifying its active compound. Sample of a bract flower of temu lawak was extracted with n-hexane, ethyl acetate, and methanol as well as distillation of its essential oil. Tyrosinase inhibition (monophenolase and diphenolase) and antioxidant (diphenylpicryl hydrazyl (DPPH) method) assays were performed on various extracts and essential oil of the sample. The ethyl acetate extract was the active extract with IC50 1.97 mg/mL on monophenolase, 1.57 mg/mL on diphenolase and 12.68 μg/mL on antioxidant. The ethyl acetate extract that was fractionated with column chromatography (CC) produced 12 fractions. F12 was the most active fraction from fractionation by CC that might contain flavonol-related compound based on infrared spectrophotometer analysis and phytochemical assay. A fraction with the highest yield (F1) continued fractionation by preparative thin layer chromatography (TLC) produced 2 fractions. F1.2 was the more active fraction resulted F1 fractionation using preparative TLC which might contain auron-type compound based on infrared spectrophotometer analysis and phytochemical assay
Keyword: antioxidant, Curcuma xanthorrhiza, tyrosinase inhibitor, fractionation

Judul: Penapisan Senyawa Aktif Bunga dan Daun Temulawak (Curcuma xanthorrhiza) sebagai Antioksidan
Abstrak: This study aimed to screen antioxidant compounds on leaves and flower of C. xanthorrhiza using DPPH (2,2-diphenyl-1-picrylhydrazil) method. The flower and leaves were extracted with n-hexane, ethyl acetate, and methanol. Essential oil of the leaves was obtained through water distillation. Each extract and essential oil were determined for theirs antioxidant activities. The ethyl acetate extract of leaves had the best radical scavenging activity with IC50 41.50±7.80 μg/mL but not as good as ascorbic acid as the positive control with IC50 3.36±0.29 μg/mL. Fractionation performed on ethyl aceate leaves extract using silica gel column chromatography by step gradient elution with n-hexane, ethyl acetate, and methanol. Fractionation resulted 13 fractions (F1-F13) and the most active fraction was the F11 fraction (IC50 24.05±2.38 μg/mL). Fraction F11 was separated further by preparative thin layer chromatography and gave 8 fractions (F11.1-F11.8). Fractions F11.1 had the highest antioxidant activity with IC50 of 28.22±7.35 μg/mL. Phytochemical assay and infrared spectroscopy on F11.1 showed that the active compound as the antioxidant was flavonol. Keywords: C. xanthorrhiza, leaves, antioxidant, flavonol
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