Judul: Potensi dan Karakteristik Bio-ekologis Tumbuhan Myrmecodia spp. di Taman Nasional Wasur Merauke Papua
Abstrak: Tumbuhan sarang semut termasuk salah satu spesies tumbuhan obat yang dapat menyembuhkan berbagai macam penyakit. Tumbuhan tersebut di Taman Nasional Wasur Merauke belum dilakukan upaya pembudidayaan dan pemanenannya masih dilakukan dari alam. Informasi tenang potensi dan karakteristik bio-ekologis belum tersedia dan teknik pengelolaannya bagi ketersediaan di alam dan budidayanya belum tersedia. Penelitian ini dilakukan dengan tujuan untuk: (1) mengidentifikasi bioekologis tumbuhan obat sarang semut, (2) menentukan potensi tumbuhan obat sarang semut berdasarkan tipe penutupan lahan di TN Wasur, (3) menentukan interaksi karakteristik bio-ekologis dengan kerapatan tumbuhan sarang semut di kawasan TN Wasur, dan (4) menentukan preference tumbuhan sarang semut terhadap jenis tumbuhan inang. Dalam penelitian ini peubah yang diamati dan diukur adalah diameter jenis vegetasi tingkat pohon tumbuhan inang, diameter jenis vegetasi tingkat pohon tumbuhan inang, diameter jenis vegetasi tingkat pohon tumbuhan inang, kerapatan tumbuhan habitus epifit, kerapatan tingkat pohon tumbuhan inang, kerapatan tingkat tiang tumbuhan inang, kerapatan tingkat pancang tumbuhan inang, jumlah spesies tumbuhan inang tingkat pohon, tiang dan pancang dan persentase keterbukaan tajuk.
Keyword: 

Judul: Studi pemanfaatan sarang semut (Myrmecodia pendans Merr. & Perry) oleh masyarakat sekitar taman nasional Wasur
Abstrak: Wasur National Park O;VNP) is located in Merauke Regency, south of Papua Province. In the early of 2006, was introduced one of herbal &om Papua which has attrach of great attention of public which is sarang semut (Mynitecodia pendans). Sarang semut can be use to healing serious diseases and to making survival of live more longer, so that the price of Sarang Semut increase drasticly. The aims of this research were to dig the knowledge and cultural of community, Marind Sendawi Anim tribe, in using medical plants especially Sarang Semut (Myrt~tecodiap endans) in traditionally. The research was conducted Erom April to May 2008 in Wasur National Park, and interview method was used with respondents who considered knows about using plants medical (snowball method). Data which obtained can be tabulated systematically and analyzed descriptively. Results show that Sarang Semut was used by traditional people (Kanume tribe, Marori tribe, Men-Gei tribe, Yeinan tribe, and Marind tribe) for 3 things: poultry woof, medical plant and economy (sources of revenue). There are six beneficial's for traditional healing i.e. stiff of muscles, kidney disorder, dizzy, give birth to, stiff and waist. Tuber is the part of this plant that was used for medical. Its tuber was boiled either burned before used. Another using with trading, increase the welfare of people. Trading schema is Erom harvester to collector and then to consumer. There is variation in each step of trading schema. Prices in harvester level ranged from Rp. 5.0001sach - Rp. 20.000lsach, consumer 1 (grosser) ranged &om Rp. 25.000 - Rp. 60.0001 kg.
Keyword: 

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