Judul: Belawan putih (Tristaniopsis whiteana): antibacterial compounds and their distribution in Peat and Heath Forests Central Kalimantan
Abstrak: Biodiversity of peat forest ecosystem is highly potential. Among these potentials is the use of forest plants as medicines. One of medicinal plants found in the peat swamp forest ecosystem is from genus Tristaniopsis, for example belawan putih (Tristaniopsis whiteana). Several studies have reported the potential of this plant as an antibacterial drug. In this report, bioactive antibacterial compounds of belawan putih leaves extract and mapping the distribution of the plant in peat and heath forests, Central Kalimantan, were studied. The sample was extracted gradually using four different solvents based on polarity (n-hexane, chloroform, ethyl acetate, and methanol). The chloroform extract showed the most active in inhibiting Escherichia coli and Staphylococcus epidermidis. Isolation and purification of the chloroform extract gave 2 pure compounds, compound 1 and 2, having the same minimum inhibitor concentration (MIC) value against E. coli were 128 μg/mL. MIC values of compound 1 and 2 against S. epidermidis were 128 and > 128 μg/mL, respectively. Based on structure elucidation using nuclear magnetic resonance proton (1H-NMR), compound 1 was estimated to have aromatic, alkoxy, methine, methylene, and methyl groups. Structure elucidation using ultraviolet and infrared spectrophotometers, 1H-NMR, 13C-NMR, DEPT, HMBC, and HMQC, compound 2 was found be betulinic acid. This is the first report of the occurrence of this compound in belawan putih and genus Tristaniopsis. One potential of belawan putih commonly used by people around forest areas is the use as medicines. Therefore, it is necessary to protect this species from the threat of extinction. One of the survey criteria in determining priority to protect the species is to find the distribution pattern of the species population. Ecological parameters of belawan putih in heath and peat forest Bawan, Central Kalimantan, was observed and measured on 6 plot in each forest by purposive sampling method. The plot was circle with radius of 10 m, so the 6 plot have area of 0.5 hectares. Based on Morissita method, the distribution pattern of belawan putih in heath and peat forest was clumped type.
Keyword: antibacterial, Central Kalimantan, heath forest, peat forest, Tristaniopsis whiteana

Judul: Belawan Putih (Tristaniopsis whiteana): Antibacterial Compounds and Their Distribution in Peat and Heath Forests Central Kalimantan.
Abstrak: Biodiversity of peat forest ecosystem is highly potential. Among these potentials is the use of forest plants as medicines. One of medicinal plants found in the peat swamp forest ecosystem is from genus Tristaniopsis, for example belawan putih (Tristaniopsis whiteana). Several studies have reported the potential of this plant as an antibacterial drug. In this report, bioactive antibacterial compounds of belawan putih leaves extract and mapping the distribution of the plant in peat and heath forests, Central Kalimantan, were studied. The sample was extracted gradually using four different solvents based on polarity (n-hexane, chloroform, ethyl acetate, and methanol). The chloroform extract showed the most active in inhibiting Escherichia coli and Staphylococcus epidermidis. Isolation and purification of the chloroform extract gave 2 pure compounds, compound 1 and 2, having the same minimum inhibitor concentration (MIC) value against E. coli were 128 μg/mL. MIC values of compound 1 and 2 against S. epidermidis were 128 and > 128 μg/mL, respectively. Based on structure elucidation using nuclear magnetic resonance proton (1H-NMR), compound 1 was estimated to have aromatic, alkoxy, methine, methylene, and methyl groups. Structure elucidation using ultraviolet and infrared spectrophotometers, 1H-NMR, 13C-NMR, DEPT, HMBC, and HMQC, compound 2 was found be betulinic acid. This is the first report of the occurrence of this compound in belawan putih and genus Tristaniopsis. One potential of belawan putih commonly used by people around forest areas is the use as medicines. Therefore, it is necessary to protect this species from the threat of extinction. One of the survey criteria in determining priority to protect the species is to find the distribution pattern of the species population. Ecological parameters of belawan putih in heath and peat forest Bawan, Central Kalimantan, was observed and measured on 6 plot in each forest by purposive sampling method. The plot was circle with radius of 10 m, so the 6 plot have area of 0.5 hectares. Based on Morissita method, the distribution pattern of belawan putih in heath and peat forest was clumped type.
Keyword: antibacterial, Central Kalimantan, heath forest, peat forest, Tristaniopsis whiteana

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