Judul: TOTAL PHENOLIC CONTENT, TOTAL FLAVONOID CONTENT AND ANTIOXIDANT ACTIVITY OF Curcumma xanthorriza Roxb. WITH DIFFERENCES IN VARIETIES AND SOLVENTS.
Abstrak: Temulawak (Curcuma xanthorriza Roxb.) memiliki kandungan fitokimia dan aktivitas antioksidan yang tinggi. Penelitian ini bertujuan mengukur rendemen sampel, total fenolik, total flavonoid, dan aktivitas antioksidan dari ekstrak aseton dan etil asetat temulawak Cursina 1 (C1), Cursina 2 (C2), dan Cursina 3 (C3) menggunakan metode DPPH, ABTS dan FRAP. Sampel dimaserasi lalu diuapkan dengan rotary evaporator. Rendemen tertinggi dihasilkan oleh ekstrak C2/aseton sebesar 5,8% dan C2/etil asetat sebesar 4,3%. Kandungan fenolik tertinggi pada ekstrak C2/aseton sebesar 157,95 mg GAE/g DW dan ekstrak C3/etil asetat sebesar 115,77 GAE/g DW, kandungan flavonoid tertinggi pada C2/aseton 338,53 QE/g dan C3/etil asetat 218,54 mg QE/g DW. Uji DPPH menunjukkan sampel C2/aseton dan C3/etil asetat memiliki aktivitas antioksidan tertinggi sebesar 54,22 dan 52,33 μM TE/g DW. Uji ABTS memiliki hasil tertinggi pada C2/aseton sebesar 492,34 μM TE/g DW dan C3/etil asetat sebesar 464,29 μM TE/g DW. Metode FRAP menunjukkan aktivitas antioksidan C2/aseton sebesar 1904,82 μM TE/g DW dan C3/etil asetat sebesar 1846,36 μM TE/g DW. Aseton merupakan pelarut terbaik untuk varietas C2 dan etil asetat untuk varietas C3
Keyword: antioksidan, aseton, etil asetat, temulawak, varietas

Judul: Variation of Antioxidant Activity and Total Flavonoid Content of The Rhizome  of Curcuma xanthorrhiza Roxb. with Three Different Cultivation Locations
Abstrak: Temulawak (Curcuma xanthorrhiza Roxb.) dapat menghasilkan metabolit sekunder dan memiliki peran sebagai sumber antioksidan. Temulawak dapat menghasilkan variasi akumulasi metabolit sekunder jika ditanam pada lokasi dengan kondisi lingkungan yang berbeda-beda. Penelitian ini bertujuan memperoleh pola interaksi genotipe temulawak dengan lingkungan penanaman multilokasi (Bogor, Cianjur, dan Sukabumi) serta memperoleh hasil evaluasi stabilitas dari setiap genotipe. Kadar total flavonoid temulawak tertinggi diperoleh dari penanaman di Sukabumi dengan nilai 21,3791 mg QE/g bobot kering. Genotipe dengan kadar total flavonoid tertinggi adalah G6 dengan nilai 23,2428 mg QE/g bobot kering. Aktivitas antioksidan tertinggi berdasarkan lokasi diperoleh dari penanaman di Sukabumi dengan nilai 9,6371 μmol TE/g bobot kering dan berdasarkan genotipe yakni G3 dengan nilai 9,1943 μmol TE/g bobot kering. Keragaman ulangan berpengaruh nyata pada parameter kadar total flavonoid dan tidak ditemukan interaksi genotipe dan lingkungan pada kedua parameter. Genotipe yang stabil pada ketiga lokasi (Bogor, Cianjur, Sukabumi) adalah G5 untuk kadar total flavonoid dan G3 dan G4 untuk aktivitas antioksidan. Genotipe G5 memiliki potensi stabil pada lokasi penanaman di Bogor dengan parameter aktivitas antioksidan.
Keyword: antioxidant, flavonoids, multilocation, temulawak

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