Judul: Persepsi masyarakat terhadap manfaat kesehatan dan pengembangan produk minuman fungsional dari ekstrak daun hantap (Sterculia oblongata R.Brown)
Abstrak: Indonesia is known by its diversity, especially ini herb-medicinal plants. The aims of this study was explore the health benefit and development of product of hantap leaves as functional drink by a community perception test. The study was conducted in April to December 2010. Study on product development was conducted at Laboratory of Food Processing, Laboratory of Organoleptic Test, and Laboratory of Food-Chemistry Analysis, Department of Community Nutrition- Faculty of Human Ecology, Bogor Agricultural University. Survey and perception test were conducted in sub district of Cicurug, Cidahu, and Parungkuda, district of Sukabumi. Respondent in this perception test study are 87 of men and women aged 20-60 years old and lived in resident of sub district of Cicurug, Cidahu, and Parungkuda, in district of Sukabumi,.
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Judul: Pengembangan Minuman Fungsional Sumber Serat Dan Antioksidan Dari Daun Hantap (Sterculia oblongata R. Brown.)
Abstrak: Sterculia oblongata R. Brown or in Sundanese known as Hantap has been used as expectorant, sore throat, stomachache, and constipation medicine. The health benefit of those was related to its phytochemical content, chlorophyll and dietary fiber. Due to limited information to hantap research, this research become important. The main goal of this research was to study the production of hantap’s leaves drink as antioxidant and dietary fiber source. Antioxidant activity (AA) and dietary fiber content (DFC) were analysed by DPPH and Enzymatic methode. Leaves contained alkaloid, sapoin, tanin, flavonoid, triterpenoid, steroid and glicoside. Leaves’s AA correlated positively to total phenol content (TPC) (r=0,9914). The leaves had AA about 42,75-47,66% (133,46 mg vitamin C/100 gram) (wb), TPC about 919,47 mg Galic Acid Equivalen (GAE) (db) or 326,16 (wb) GAE and 54,76 mg vitamin C (db) or 19,43 mg vitamin C (wb). The leaves contained chlorophyll about 5245,81 μg/gram and could be the source of dietary fiber with soluble (SDF) and insoluble fiber (IDF) content 4,5% dan 15,67% respectively. The Hantap’s Leave Functional Drink (HLFD) formula was produced by extracting the leaves with leaves-water ratio 1:15. The extraction used two methode namely hand squezzing (R) and blender (B) machine. Then the leaves extract mixed with water melon essens about 0,4% and sugar about 15%. General acceptancy level of the HLFD was respectively 72,41% and 86,20% for B and R. HLFD characteristics were moderate acid (6,00-6,26), moderate viscous (300-450 Cp) and have total solid dissolved (TDS) that supposed as soluble dietary fiber (13,25-13,5 0Brix). AA of R about 50,87%-64,61% (3,92-4,99 AEAC). Adversly, B antioxidant activity was about 38,69%-44,44% (2,95-3,39 AEAC). Chlorophyl content of HLFD was 800,49 μg/gram and 1194,06 μg/gram respectively for R and B. Soluble fiber content of B was higher than R respectively 0,353% (wb) dan 0,134% (wb). Adversly insoluble fiber content for R was higher than B respectively 0,310% (wb) and 0,243% (wb).
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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