Judul: Potensi keanekaragaman jenis tumbuhan di Taman Nasional Kerinci Seblat studi kasus di resort gunung tujuh dan Kecamatan Kayu Aro, Kabupaten Kerinci, Jambi
Abstrak: Kawasan Taman Nasional Kerinci Seblat (TNKS) merupakan salah satu sumberdaya alam yang sangat penting, karena menguasai hajat hidup masyarakat dan dapat menunjang pembangunan ekonomi daerah maupun nasional. Manfaat yang dapat diperoleh dari tumbuhan beranekaragam, baik berupa kayu maupun non kayu. Jenis-jenis tumbuhan berguna berupa non kayu yang sangat potensial, antara lain: tumbuhan obat, tumbuhan hias, tumbuhan aromatik, tumbuhan penghasil pangan, tumbuhan penghasil pakan ternak/satwaliar, tumbuhan penghasil pestisida nabati, tumbuhan penghasil bahan pewarna dan tanin, tumbuhan penghasil minuman, tumbuhan untuk upacara adat, tumbuhan penghasil kayu bakar, tumbuhan penghasil bahan bangunan dan tumbuhan penghasil tali, anyaman dan kerajinan.
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Judul: Perencanaan Lanskap Areal Wisata Alam Gunung Tujuh, Taman Nasional Kerinci Seblat, Propinsi Jambi
Abstrak: Indonesia terkenal dengan kekayaan sumberdaya alamnya. Salah satu sumberdaya alam Indonesia yang mempunyai potensi untuk dikembangkan, adalah Taman Nasional. Taman Nasional Kerinci Seblat (TNKS) adalah salah satu dari 34 Taman Nasional yang ada di Indonesia, dan merupakan Taman Nasional yang terluas dan terpenting di Asia (MacKinnon dan MacKinnon, 1986). Salah satu pemanfaatan yang ekonomis dari suatu taman nasional adalah sebagai kawasan wisata, sehingga diperlukan suatu perencanaan yang baik untuk mengoptimalkan bahkan memaksimalkan pemanfaatannya.
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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