Judul: Kajian Pengembangan Kawasan Konservasi  Terumbu Karang Di Pulau Waidoba Kecamatan  Kayoa, Propinsi Maluku Utara
Abstrak: Penelitian bertujuan untuk mengetahui kondisi perairan dan ekosistem terumbu karang sebelum dikonservasi, menentukan batas dan zonasi kawasan konservasi terumbu karang sesuai kualitas perairan, kriteria ekologis, ekonomi, dan sosial, serta menentukan arahan kebijakan dan strategi pengelolaannya. Penelitian dilakukan di pulau Waidoba kecamatan Kayoa, propinsi Maluku Utara dan berlangsung pada bulan April sampai Juli 2002.
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Judul: Kajian kesesuaian perairan pesisir desa Kawai Kabupaten Maluku Tengah bagi pengembangan ekowisata
Abstrak: Kegiatan wisata yang sedang berjalan di Desa Sawai saat ini berhadapan dengan berbagai masalah yaitu: (a) indiiasi kerusakan terumbu karang pada beberapa bagian perairan, (b) tidak semua pemangku kepentingan terlibat dalam pengelolaan kegiatan wisata, dan (c) belum terarahnya pengelolaan wisata saat ini. Oleh karena itu penelitian ini dilakukan dengan tujuan menganalisis kondisi dan potensi sumberdaya pesisir dan laut, sebagai sumberdaya utarna pengembangan kegiatan ekowisata, mengindentifikasi para pemangku kepentingan yang terlibat dalam kegiatan ekowisata, menganalisis kesesuaian kawasan dan menentukan strategi pengelolaan ekowisata ke depan. Penelitian berlangsung pada bulan November 2008 yang meliputi: (1) survei awal dan koleksi data sekunder, (2) pengumpulan data primer yang terdiri atas pengumpulan data kondisi terumbu karang, ikan karang, parameter lingkungan dan data sosek, (3) analisis data dan penulisan laporan. Metode identifikasi kondisi ikan karang dan terumbu karang memjuk English et al. 1994. Pengambilan data sosial ekonorni dilakukan melalui wawancara kepada responden dan juga melalui penelusuran data sekunder. Analisis kesesuaian ekowisata bahari dan daya dukung kawasan dilakukan merujuk Yulianda (2007).
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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