Judul: Situs Web sebagai Media Pendidikan Konservasi: Studi Kasus Taman Nasional Gunung Gede Pangrango
Abstrak: Pemahaman tentang konservasi dapat ditingkatkan dengan penggunaan media pendidikan. Saat ini, media pendidikan tidak hanya berbentuk cetak, tapi juga berbentuk digital contohnya situs web. Salah satu institusi yang menggunakan situs web adalah taman nasional yang memiliki fungsi optimal untuk pendidikan konservasi. Pemilihan Taman Nasional Gunung Gede Pangrango National Park (TNGGP) sebagai studi kasus penelitian didasari dari visi TNGGP, yaitu menjadi pusat pendidikan konservasi kelas dunia. Tujuan dari penelitian ini adalah untuk menganalisis kesesuaian situs web TNGGP sebagai media pendidikan konservasi. Metode yang digunakan yaitu wawancara, observasi situs web, dan analisis isi. Dari hasil penelitian ditemukan 16 terbitan yang termasuk domain pengetahuan, 6 terbitan domain sikap, dan 1 terbitan domain keterampilan. Situs web TNGGP sebagai media pendidikan konservasi telah mampu memenuhi domain pengetahuan namun perlu ditambahkan untuk domain sikap dan keterampilan.
Keyword: media pendidikan konservasi, pendidikan konservasi, situs web, TNGGP

Judul: Perencanaan Pendidikan Konservasi di Taman Nasional Gunung Halimun
Abstrak: Taman Nasional Gunung Halimun (TNGH) adalah salah satu kawasan konservasi di Jawa Barat yang salah satu tujuamya adalah menyelenggarakan kegiatan pendidikan konservasi kepada masyarakat baik itu ~nasyarakat sekitar maupun masyarakat umum lainnya. Pelaksanaan kegiatan pendidikan konservasi tersebut harus disesuaikan deiigan kondisi taman nasional itu sendiri dan karakteristik dari masyarakat tersebut. Untuk itu perlu disusun perencanaannya terlebih dahulu. Tujjuan penelitian ini adalah untuk lncnyusun perencanaan pendidikan konservasi di TNGH.
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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