Judul: Aktivitas Antioksidan Zat Ekstraktif Daun Mangium (Acacia mangiumWilld) Berdasar Uji secara In Vitro dan In Vivo.
Abstrak: Tujuan penelitian ini adalah menentukan rendemen dari ekstraksi daun mangium dengan pelarut air dan etanol, menganalisis kandungan total fenol, dan menetapkan aktivitas antioksidan secara in vitro pada ekstrak yang dihasilkan. Berdasar nilai rendemen dan aktivitas antioksidan tertinggi dipilih ekstrak paling prospektif kemudian diuji aktivitas antioksidan secara in vivo. Ekstraksi dilakukan menggunakan metode perebusan air dan sokletasi dengan etanol 30, 70, dan 100%. Aktivitas antioksidan in vitro diuji dengan metode 1,1-difenil-2-pikrilhidrazil (DPPH) dan secara in vivo dengan mengukur kadar malondialdehida (MDA) serum darah mencit. Rendemen ekstrak daun mangium tertinggi yaitu ekstrak etanol 30% diikuti ekstrak air, etanol 70, dan 100%. Aktivitas antioksidan yang dihasilkan dari keempat ekstrak tergolong sangat kuat. Aktivitas antioksidan ekstrak daun mangium etanol 30% tidak berbeda nyata dengan etanol 70% dan vitamin C. Ekstrak etanol 30% daun mangium dengan dosis 0.867 mg/kg/bb mencit/hari selama 7 hari mampu menurunkan kadar MDA serum dari 0.229±0.031 menjadi 0.090±0.055 μmol.
Keyword: difenil-2-pikrilhidrazil (DPPH), aktivitas antioksidan, ekstrak daun mangium, malondialdehida (MDA), mencit

Judul: Aktivitas Antidiabetes Ekstrak Kulit Mangium (Acacia mangium Willd.) Melalui Uji Penghambatan Enzim α-Glukosidase Secara In Vitro
Abstrak: Mangium (Acacia mangium Willd.) bark extract is categorized as active in inhibiting the activity of α-glucosidase enzyme by having IC50 ≤ 100 μg/mL. The purposes of this research are to determine mangium bark extract yield, determining its antidiabetic activity using in vitro α-glucosidase enzyme inhibition test, and analyze chemical components of the most active extract as α-glucosidase enzyme inhibitor. Extraction was conducted using boiling method with water and soxletation using various concentration of ethanol. Extraction of mangium using water, 30, 70, and 100% ethanol yielded 14.14, 11.72, 8.03, and 14.06% respectively. All extratcts are categorized as active in inhibiting the activity of α-glucosidase. Mangium bark extract with highest antidiabetic activity is ethanol 30% extract (IC50= 29.36 μg/mL), followed by 70% ethanol extract (IC50= 33.90 μg/mL), water extract (IC50= 35.14 μg/mL), and 100% ethanol extract (IC50= 35.49 μg/mL). Chemical component analysis results show that 30% ethanol extract contains dominant compound from phenolic group and alkaloids which inhibiting the activity of α-glucosidase enzyme.
Keyword: alkaloids., flavanoids, α-glucosidase, antidiabetic, mangium bark

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