Judul: Identifikasi dan Potensi Antioksidan Flavonoid Kulit Kayu Mahoni (Swietenia macrophylla King)
Abstrak: Mahogany bark as wood processing industrial waste has not widely used compared to other parts of mahogany. This research was conducted to identify the flavonoids from mahogany bark and its role as an antioxidant. Antioxidant activity of mahogany bark’s extract was measured by using two methods namely diphenil-1.1-2-picrylhydrazyl (DPPH) method and tiobarbituric acid (TBA) method. Identification of flavonoids (quersetin) was performed by using HPLC and TLC. Antioxidant activity of flavonoids (quersetin) was measured by using DPPH method.Inhibition activity of mahogany bark’s extract on DPPH method for the extraction of methanol, n-hexane, diethyl ether, ethyl acetate, vitamin E (positive control) and water at concentrations of 50 ppm are respectively for 81.33%, 12.74%, 23:40%, 65.54 %, 30.42%, and 68.04%. The inhibition activity of mahogany bark’s extract on TBA methods for methanol fraction 200, 100, 50 ppm was 59.81%, 59.02%, 58.96%. Extraction of water 200, 100, 50 ppm was 54.65%, 49.01%, 43.09%. Vitamin E 200, 100, 50 ppm was 40.91%, 36.48%, 27.15%. Kuersetin content in the sample is 16 mg/100g sample. Inhibition activity of antioxidants on kuersetin as 50, 100, and 200 ppm for 87.2%, 89.1%, 92.8%.
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Judul: Aktivitas Antioksidan dan Penghambatan α-Glukosidase Ekstrak dan Nanopartikel Ekstrak Kulit Kayu Mahoni (Swietenia macrophylla King)
Abstrak: Mahogany (Swietenia macrophylla King) bark is waste disposal industry manufacture of bark. Mahogany bark have catechin, epicatechin, and swietemacrophyllanin contents which useful as an antioxidants and antidiabetic of rats induced alloxan. This study aimed to determine the antioxidant activity and inhibition of α-glucosidase in the extract and nanoparticle extract of mahogany bark. Extraction method use aquades as solution, antioxidant activity assays was caried using 2,2-diphenyl-1-picrilhidrazyl (DPPH) and reducing power. Inhbition of α-glucosidase was measured with a spectrophotometric method. Mahogany moisture content was 4.87%. Extract yield of 9.46% and yield of nanoparticle 51.11% were obtained. The DPPH resulted that of the rutin compound, extract, and nanoparticles obtained by IC50 DPPH method sequentially 17.33 ± 1.17 ppm, 9.62 ± 0.72 ppm, and more than 2500 ppm. Respectively, power reduction of the rutin compound has a 67.03% at 25 ppm, the extract 67.41% at 20 ppm, and nanoparticle 31.47% at 20 ppm. IC50 values inhibition of α-glucosidase extract was 17.25 ppm, nanoparticles were more than 200 ppm, and acarbose was 0.37 ppm. The test results showed that the extract capable of acting as antioxidant and antidiabetic based on IC50 values. As for nanoparticles mahogany bark extract did not able to act as antioxidants and antidiabetics based on the method tested.
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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