Judul: Total Flavonoid, Total Fenolik dan Aktivitas Antioksidan Ekstrak Metanol Temulawak yang Dibudidayakan dengan Kombinasi Pupuk N,P, dan K
Abstrak: Temulawak (Curcuma xanthorriza roxb.) dilaporkan mengandung senyawa fitokimia dan aktivitas antioksidan tinggi. Tujuan penelitian ini adalah mengukur total flavonoid, total fenolik dan aktivitas antioksidan ekstrak temulawak yang dibudidaya dengan kombinasi pupuk NPK menggunakan metode DPPH dan ABTS. Sampel diekstraksi menggunakan maserasi bertingkat dan dievaporasi dengan vacuum evaporator. Kandungan total flavonoid tertinggi yaitu sampel dengan pupuk NPK sebesar 50±23,81 mg QE/bobot kering dan kandungan total fenolik tertinggi juga terdapat sampel dengan pupuk NPK sebesar 34,67±3,79 mg GAE/bobot kering. Uji antioksidan metode DPPH menunjukkan sampel dengan pupuk NK memiliki aktivitas antioksidan tertinggi sebesar 169,67±8,5 µmol TE/g bobot kering. Hasil berbeda pada metode ABTS pada sampel dengan pupuk NPK sebesar 261,67±29,69 µmol TE/g bobot kering. Pupuk NPK berpotensi sebagai pupuk terbaik untuk meningkatkan kandungan metabolit sekunder dan aktivitas antioksidan temulawak., Temulawak (Curcuma xanthorriza roxb.) is reported to contain phytochemicals and high antioxidant activity. The purpose of this reasearch was to measure the flavonoid content, phenolic content, and the antioxidant activity of methanol extract of temulawak cultivated by using NPK fertizer combination. The samples were extracted with graded maceration and evaporated with vacuum evaporator. The extracts were subjected to total flavonoid, total phenolic, and antioxidant activity assays. The antioxidant activity was measured using the DPPH and ABTS methods. The highest total flavonoid content was sample from NPK fertilizer of 50±23,81 mg QE/dry weight, and the highest phenolic content was from NPK fertilizer of 34,67±3,79 mg GAE/dry weight. The antioxidant test using the DPPH method showed that the extract from NK fertilizer had the highest antioxidant activity of 169,67±8,5 µmol TE/dry weight. Different results were obtained in ABTS method, was found in extract with NPK fertilizer is 261,67±29,69 µmol TE/g dry weight. NPK fertilizer gave the best result for increase secondary metabolites and antioxidant activity of temulawak.
Keyword: antioxidant, NPK, temulawak, flavonoid, phenolics

Judul: Produktivitas Kurkuminoid dan Aktivitas Antioksidan Temulawak (Curcuma xanthorrhiza Roxb.) Dibudidaya dengan Kombinasi Pupuk N, P, dan K
Abstrak: Temulawak (Curcuma xanthorrhiza Roxb.) termasuk tanaman obat yang mengandung senyawa kurkuminoid dan memiliki aktivitas antioksidan. Penelitian ini bertujuan menentukan kombinasi pupuk N, P, K terbaik berdasarkan kadar senyawa kurkuminoid dan aktivitas antioksidan pada rimpang temulawak, serta menentukan korelasi antara keduanya. Kadar kurkuminoid ditentukan dengan High Performance Liquid Chromatography (HPLC), sedangkan aktivitas antioksidan diukur menggunakan metode Ferric Reducing Antioxidant Power (FRAP) dan Cupric ion Reducing Antioxidant Capacity (CUPRAC). Kadar kurkuminoid tertinggi terdapat pada perlakuan K, P, NK, dan NPK dengan nilai masing-masing 8,61; 8,50; 7,99; dan 7,04 mg/g bobot kering. Aktivitas antioksidan tertinggi metode FRAP terdapat pada pemupukan kombinasi NK dan NPK dengan nilai 43,24 dan 41,76 µmol TE/g bobot kering, sedangkan pada metode CUPRAC terdapat pada pemupukan kombinasi NPK sebesar 307,11 µmol TE/g bobot kering. Aktivitas antioksidan berbanding lurus dengan kandungan kurkuminoid berdasarkan uji korelasi. Pupuk NPK merupakan kombinasi pupuk terbaik dalam meningkatkan kandungan kurkuminoid dan aktivitas antioksidan temulawak., Temulawak (Curcuma xanthorrhiza Roxb.) is a medicinal plant that contains curcuminoid compounds and has antioxidant activity. This study aims to determine the best combination of N, P, K fertilizers based on levels of curcuminoid compounds and antioxidant activity in temulawak rhizome, and determine correlation between them. The curcuminoid level was determined by HPLC, while the antioxidant activity was measured using the FRAP and CUPRAC methods. The highest curcuminoid levels were found in the K, P, NK, and NPK treatments with a value of 8,61; 8,50; 7,99; and 7,04 mg / g dry weight, respectively. The highest antioxidant activity of the FRAP method was found in the combination of NK and NPK fertilization with values of 43,24 and 41,76 µmol TE / g dry weight, while in the CUPRAC method was found in NPK combination fertilization with value 307,11 µmol TE / g dry weight. Antioxidant activity was directly proportional to curcuminoid content based on correlation test. NPK fertilizer is the best combination to increase the curcuminoid content and antioxidant activity of Curcuma xanthorrhiza.
Keyword: antioxidant, curcuminoid, java turmeric, NPK fertilizer

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