Judul: Karakteristik Nanoemulsi Temulawak
Abstrak: Curcumin is the active compound that has many benefits for health, so the potential for development in the pharmaceutical. However, the development potential is limited by the natural characteristics of curcumin. Curcumin is known to have a low solubility in the gastrointestinal tract so it is relatively difficult to get into the blood plasma. In addition, curcumin also degraded easily by free radicals agent necessitating the addition of other antioxidants. One effort to overcome these problems is the manufacture nanokurkumin particles in the form of an emulsion (nanoemulsion). Nanoemulsion is made with high-speed homogenization method at 24 000 rpm for 40 minutes, in order to obtain a grain diameter of 32.62 ± 1.84 nm with a polydispersity index of 0255 ± 0013. Curcumin in preparation nanoemulsi have characteristics include bioavailability, solubility, and stability of droplet size. Nanoemulsion bioavailability test conducted by Franz diffusion method. Solvents used for the solubility test is hexane, water, ethanol, methanol, and acetone. Nanoemulsi droplet size stability tested by storage at 40C, room temperature and a temperature of 500C with a 90th day storage period. Curcumin in preparation nanoemulsi showed an increase absorption (bioavailability) of 45.97 % compared to the emulsion. In the solubility test changes the solubility of the preparations nanoemulsi than emulsion preparations in various solvents, suggesting a change in solubility characteristics. Nanoemulsi has the best solubility in ethanol with the percent dissolved at 96.74%. Nanoemulsi investigated have better stability than the emulsion preparation at various storage temperatures. Nanoemulsi ginger has better dimensional stability at 40C temperature storage with storage time of 60 days. The addition of citric acid (antioxidant) on nanoemulsi can inhibit decreased levels of curcumin.
Keyword: Bogor Agricultural University (IPB), stability., bioavailability, nanoemulsion, curcumin, Temulawak

Judul: Aktivitas Antiinflamasi Sediaan Nanopartikel Ekstrak Kurkuminoid Temulawak Tersalut Asam Palmitat secara In Vivo
Abstrak: Temulawak rhizome contains curcuminoid which have pharmacological activities as an anti-inflammatory. However, oral bioavailability of curcuminoid are very low in the rat and human body. This problem can be overcome by the incorporation of curcuminoid compounds in colloidal carrier systems known as solid lipid nanoparticle (SLN). The objective of this research was to determine the in vivo anti-inflammatory activity of temulawak curcuminoid extract nanoparticles coated with palmitic acid. The in vivo anti-inflammatory activity of this nanoparticle was evaluated by measuring the ability of nanoparticles in reducing swelling (edema) in paw rats because of carrageenan induced 1%. Nanoparticles of curcuminoid temulawak successfully made with the average size of 99.78 nm and polydispersity index of 0.3550. Nanoparticles curcuminoid can increase the antiinflammatory activity of curcuminoid compounds. The results confirmed, the largest percentage of anti-inflammatory power of nanoparticle curcuminoid extract equivalent to Na-diclofenac.
Keyword: Bogor Agricultural University (IPB), palmitic acid, nanoparticles, curcuminoids, anti-inflammatory

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