Judul: Hasil Fermentasi Beras Putih Petang, Bali : Hubungan Antara Kadar Total Fenol dan Aktivitas Antioksidan
Abstrak: Beras putih (Oryza sativa L) memiliki kandungan antioksidan karena memiliki senyawa fenolik seperti flavanoid. Kandungan ini baik untuk menangkal radikal bebas dan mencegah kerusakan kulit. Fermentasi diduga mampu meningkatkan kadar fenolik dan aktivitas antioksidan pada kosmetik beras. Persiapan dilakukan dengan menyiapkan 3 sampel fermentasi nasi yang divariasikan 6 jam, 12 jam dan 24 jam. Uji yang dilakukan adalah uji pH, uji aktivitas antioksidan, dan uji total fenol. Fermentasi terbukti meningkatkan aktivitas antioksidan dan kadar fenol. Hasil menunjukkan bahwa kadar antioksidan terbaik pada sampel 24 jam yaitu 153.944±57.47 ppm AEAC, memiliki pH 4.76 dan kadar fenol 31.61 GAE. Nilai regresi pada persamaan kadar fenol dan aktivitas antioksidan menunjukkan bahwa peningkatan kadar fenol memiliki korelasi signifikan terhadap peningkatan aktvitas antioksidan.
Keyword: antioksidan, fermentasi, Oryza sativa L, total fenol

Judul: Aktivitas Antioksidan dan Total Fenol pada Hasil Fermentasi Beras Hitam dengan Khamir (Saccharomyces cerevisiae).
Abstrak: Beras hitam (Oryza sativa L. indica) adalah tanaman dari famili Poaceae, mengandung aktivitas antioksidan tinggi yang kaya antosianin. Tujuan dari penelitian ini adalah menentukan lama waktu fermentasi beras hitam dengan khamir (Saccharomyces cerevisiae) yang optimum untuk menghasilkan aktivitas antioksidan terbaik. Pengukuran aktivitas antioksidan menggunakan uji AEAC (Ascorbic acid Equivalent Antioxidant Capacity) dan uji GAE (Gallic Acid Equivalent). Prinsip uji AEAC membandingkan aktivitas antioksidan sampel dengan asam askorbat dan uji GAE membandingkan konsentrasi senyawa fenolik sampel dengan asam galat. Hasil penelitian menunjukkan aktivitas antioksidan tertinggi adalah pada lama waktu fermentasi 6 jam. Aktivitas antioksidannya setara dengan 234.50 ppm AEAC dan konsentrasi senyawa fenoliknya setara dengan 54.28 ppm GAE.
Keyword: beras hitaml., accharomyces cerevisiae, fermentasi, antioksidan, fenol

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