Judul: Tingkat Retensi Karoten Total dan Protein serta Aktivitas Antioksidan Masakan Torbangun (Coleus amboinicus Lour)
Abstrak: Bataknese Simalungun women in North Sumatra who consume Torbangun soup believe that it can stimulate the breast milk production. Until now information and use of Torbangun is still very limit. People need to know more about Torbangun apart from its benefit but also on the effect of cooking towards nutrients especially those sensitive to the total carotenoids and proteins as well as antioxidant activity. The purpose of this research was to study the effects of Cuisine types on the retention of carotenoids, protein, and antioxidant activity of Torbangun cuisine. There are three types of Torbangun cuisine used in this study namely lodeh, pecel, and stir-fry. Chemical analysis conducted in this study include the analysis of water, fat, protein, ash, carbohydrate cntents, total carotene, and antioxidant activity. The study used Complete Randomized Design (CRD). Data were analyzed using Analysis of Variance, Duncan, and independent T-Test. Cuisine types significantly effected the nutrient content of water, ash, carbohydrates, and carotene, while there were no significant effect of the nutrient content of fat, and protein then antioxidant activity. The contribution of vitamin A in the torbangun cuisine (per serving) was more than 20% of the recommended reference Label Nutrition (ALG), so that these products can be categorized as high provitamin A. Independent T-Test results showed that the levels of crude carotene lodeh before and after cooking were significantly different (p <0.05), whereas carotene levels of pecel and stir-fry were not significantly different, as well as protein content and antioxidant activity before and after cooking (p > 0.05).
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Judul: Evaluasi Kandungan Nutrisi Daun Torbangun (Coleus amboinicus Lour) terhadap Daya Hidup Mikroba Rumen dan Kecernaan In vitro
Abstrak: This research was conducted to examine the effect of torbangun (Coleus amboinicus Lour) in the diet on the viability of rumen microbes (bacteria and protozoa populations) and in vitro digestibility, using randomized block design with rumen liquid collecting time as a group with 5 treatments and 3 replicates. The treatments were: R0 (kontrol): 60% H + 40% K, R1: RO + 2.5% T, R2: RO + 5% T, R3: RO + 7.5% T, R4: RO + 10% T. Data were analyzed by Analysis of Variance. Further tested Contrast Ortogonal and Polynomial, if there was significant differences. Parameters that measured include: viability rumen of microorganisms (bacteria and protozoa populations), fermentability (the concentration of TVFA and N-NH3), and digestibility (dry and organic matter) in the rumen. The experiment showed that treatments significantly (P<0.05) affected dry matter digestibility and VFA concentration on additional treatment torbangun leaves as much as 2.5% in the ration (R1).
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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