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: Pengaruh Variasi Pelarut dan Waktu Ekstraksi Rimpang Temulawak terhadap Total Fenolik, Flavonoid, dan Aktivitas Antioksidan
Abstrak: Temulawak (Curcuma xanthorrhiza) mengandung senyawa metabolit sekunder seperti fenolik dan flavonoid yang berpotensi sebagai antioksidan. Senyawa metabolit sekunder yang banyak ditemukan dalam temulawak adalah kurkuminoid yang tergolong dalam senyawa polifenol. Maserasi merupakan metode sederhana untuk mengekstrak senyawa metabolit sekunder dari tumbuhan. Selama prosesnya, jenis pelarut dan waktu maserasi menjadi faktor penting yang dapat memengaruhi hasil ekstraksi. Penelitian ini bertujuan untuk menganalisis pengaruh pelarut (akuades, etanol 50%, metanol 50%, dan aseton 50%) dan waktu ekstraksi (12, 24, 36, dan 48 jam) terhadap kadar total fenolik, total flavonoid, dan aktivitas antioksidan. Hasil pengujian menunjukkan bahwa etanol 50% memberikan hasil terbaik dengan kadar fenolik 36,29 mg GAE/g, flavonoid 66,96 mg QE/g, dan aktivitas antioksidan (metode CUPRAC) 117,13, Temulawak (Curcuma xanthorrhiza) contained secondary metabolite compounds such as phenolics and flavonoids with potential antioxidant activity. The most abundant secondary metabolite compound found in temulawak was curcuminoid. Maceration was a simple method for extracting secondary metabolite compounds from plants. During maceration, the type of solvent and the maceration time were crucial factors that affected the test results. This study aimed to analyze the effect of solvent (aquadest, 50% ethanol, 50% methanol, and 50% acetone) and extraction time (12, 24, 36, and 48 hours) on total phenolics, total flavonoids, and antioxidant activity. The results showed that 50% ethanol yielded the highest phenolic content (36,29 mg GAE/g), flavonoid content (66,96 mg QE/g DW), and antioxidant activity (CUPRAC method) of 117.06
Keyword: antioxidant, phenolic, flavonoid, solvent, time

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