Judul: Suatu Studi Tentang Penangkapan Tuna oleh PT perikanan Samodra Besar, Benoa Bali
Abstrak: Sampai saat i n i usaha penangkapan tuna d i perairan i-n donesia masih memberikan kemungkinan yang baik untuk dikembangkan. Dari potensi tuna dan skipjack di perairan Indonesia yang diperkirakan sekitar 207 000 ton, namun baru t+ % yyan dikelola. Satu unit penangkapan rawai tuna terdiri dari kapal, alat penangkapan dan tenaga kerja. Kapal yang digunakan untuk operasi adalah kapal besi berukuran rata-rata 111 GT, 370 FIP dan crew sebanyak 23 orang.
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Judul: Suatu studi tentang prospek perikanan tuna di perairan Indonesia
Abstrak: Ikan tuna merupakan suatu sumber protein penting bagi umat manusia di dunia. Harga tuna yang tinggi di pasaran dunia mengakibatkan kompetisi penangkapan tuna meningkat terus. Sebagian besar dari jenis-jenis tuna utama di duga telah mencapai tingkat pengusahaan maksimum (fully exploited). Ikan tuna mempunyai daerah penyebaran yang sangat luas, hidup di daerah pantai dan di lepas pentai, di daerah tropis dan subtropis, meliputi Samudera Hindia, Pasifik den Atlantik. Penyebaran tuna sangat dipengaruhi oleh kondisi oseanografis setempat seperti arus, temperatur, salinitas, kecerahan, dan lain-lain. Penangkapan ikan tuna dilakukan dengan berbagai alat penangkapen. Diantaranya yang umum dipergunakan adalah longline, pole and line dan purse seine. Produksi tuna di dunia terutama diperoleh dari hasil tangkapan tuna long line. Di perairan Indonesia, penangkapan tuna terutama di lakukan oleh kapal-kapal asing, seperti Jepang, Korea Selatan dan Taiwan. Untuk periode tahun 1970-1978 Jepang melakukan operasi penangkapan tuns longline di perairan Indonesia rata-rata 10.905 kali, dengan hasil rata-rata 481.199 ekor setiap tahunnya. Hasil tangkapan ini didominasi oleh yellowfin (54-72%), bigeye (20-28%) dan albacore (0-8%). ...
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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