Judul: Identifikasi golongan flavoid daun jati belanda berpotensi antioksidan
Abstrak: Daun jati belanda merupakan tanaman obat yang berpotensi. sebagai antioksidan. Flavonoid diekstrak menggunakan etanol 70%, dilanjutkan dengan ekstraksi partisi etil asetat dan fraks.i. d .,i m.u..,m. ikan menggunakan kromatografi kilas dingan kdom silika gei dan ;Len kloroform-metanol(9: 1): S3I"aftiaksi' dkiigan nilai Rf sama digabungkan ke dalam vial dan dilakukan uji aktivitas antioksidan. Fraksi gabungan teraktif diidentifikasi dengan kromatografi kertas, kromatografi lapis tipis (KLT), dan spektrofotorneter Fourier transfobm? infrared (FTIR). Metode penangkapan radikal bebas DPPH (1,l-difenil-2-pikrilhidrazil) digunakan untuk uji aktivitas antioksidan
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Judul: Aktivitas Antioksidan Subfraksi Aktif Daun Jati Belanda (Guazuma ulmifolia Lamk.).
Abstrak: Ekstrak daun jati belanda (G. ulmifolia Lamk.) dikenal memiliki khasiat pelangsing tubuh dan telah diuji aktivitas antioksidan dari ekstrak kasarnya. Namun demikian, masih belum diketahui senyawa yang berperan dalam aktivitas antioksidan pada ekstrak kasar tersebut. Penelitian ini bertujuan menentukan daya antioksidan optimal dari subfraksi teraktif ekstrak daun jati belanda. Pemisahan fraksi dilakukan menggunakan metode kromatografi kolom menggunakan eluen etil asetat:aseton (1:1), kemudian pengukuran aktivitas antioksidan dengan menggunakan metode inhibisi aktivitas radikal bebas DPPH 2,2-difenil-1-pikrilhidrazil). Hasil menunjukkan fraksi aktif mencakup subfraksi F2, F3, F4, F3,4, F7, dan F12 dengan rentang IC50 sebesar 17.46-26.73 μg/mL, dan fraksi fenolik dengan IC50 14.90 μg/mL. Absorbansi maksimum untuk identifikasi awal menunjukkan puncak fraksi fenolik berada pada 330.5 nm, 282.5 nm, dan 226.5 nm, F3,4 pada 332.5 dan 286 nm, F2 pada 323.5 dan 268 nm, F7 pada 334, 287, dan 216 nm, serta subfraksi F12 menunjukkan puncak pada panjang gelombang 335.5, 288.5, dan 271 nm. Penelitian ini menunjukkan subfraksi ekstrak daun jati belanda teraktif adalah subfraksi F4 dengan IC50 17.46 μg/mL
Keyword: 2,2-difenil-1-pikrilhidrazil, Guazuma ulmifolia Lamk., kromatografi, spektrofotometer, subfraksi

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