Judul: Pengaruh Insektisida Baycarb 500 EC terhadap Pertumbuhan Burayak Ikan Mas (Cyprinus carpio L.)
Abstrak: Penelitian ini dilakukan dari tanggal 13 Juli sampai, dengan 1 September 1987 di Balai Penelitian Perikanan Air Tawar, Bogor. Tujuan penelitian adalah untuk mengetahui : (1) pengamh akut dan bioaktivitas insektisida Baycarb 500 EC terhaaap burayak ikan Mas, (2) pengamh beberapa ambang lconsentrasi insektisida Baycarb 500 EC yang dianjurkan bagi pengendalian hama Wereng Coklat (Nilaparvata lugens), terhadap laju kematian, laju pertwnbuhan dan produksi burayak ikan Mas, dan (3) nilai Maximum Acceptable Toxicant Concentration (MATC) dan Application Factor (AF). Hewan uji adalah burayak ikan Mas dengan ukuran panjang 1 - 3 cm. Burayak ikan Mas diperoleh dari peternakan PI Akuakultura Nasional, Desa Cibening, Kabupaten Bogor. Bahan uji yang digunakan adalah insektisida Baycarb 500 EC dengan bahan aktif BPMC (2-(1-methyl propyl) phenyl methyl carbamate. Penelitian ini diran~ang menurut pola rancangan acak lengkap. Konsentrasi uji adalah kontrol, 1 ppm (dosis aplikasi di sawah.), 4 ppm dan 8 ppm.
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Judul: Pengaruh lanjut insektisida sevin-85S, dianogsa-60EC dan gammexane-26DP terhadap pertumbuhan burayak ikan mas
Abstrak: Suatu penelitian untuk mengetahui pengaruh lanjut insektisida Sevin-85S, Diazinon-60EC dan Gammexane-26DP pada konsentrasi 50% dari Lethal Concentration (LC)50-24 Jam (sublethal) selama 24 jam, terhadap pertumbuhan burayak ikan Mas, telah dilakukan dari tanggal 1.0 Mei 1981 sampai dengan 29 Juni 1981 di Laboratorium Polusi Air TAB BIOTROP Bogor. Sebagai hewan uji digunakan burayak ikan Mas, berumur 13-14 hari setelah menetas, berukuran panjang 15-25 mm. Penelitian ini terdiri dari uji toksisitas akut dan uji pengaruh lanjut terhadap pertumbuhan burayak ikan Mas dari insektisida uji. Hasil uji toksisitas akut didapat- kan LC50-24 jam untuk insektisida Sevin-855, Diazinon-60EC dan Gammexane-26DP masing-masing adalah 2,40 ppm, 1,75 ppm dan 0,68 ppm. Sebagai perlakuan, hewan uji tersebut dicelupkan se- lama 24 jam pada masing-masing insektisida uji dengan konsentrasi 50% dari LC50-24 jam. Untuk perlakuan Kontrol hewan uji tidak dicelupkan pada insektisida uji. Kemudian hewan uji dipelihara selama 6 minggu dalam naра 0,6 x 0,6 x 0,75 m³, m³, yang diletakkan di perairan kolam tembok berukuran 2,6 x 4,1 x 1 m³. Jumlah kolam yang di- gunakan sebanyak 12 buah; untuk rancangan percobaan acak lengkap dengan 4 perlakuan dan. 3 ulangan. Di setiap kolam ditempatkan 2 hapa dimana 1 hapa yang diisi 100 ekor burayak untuk analisa pertumbuhan dan 1 hapa lagi untuk persediaan burayak untuk menjaga agar kepadatan burayak pada hapa pertumbuhan tetap. Burayak diberi makan setiap hari dari campuran kuning telur dengan terigu (1:1) sebanyak 10% dari berat total burayak yang jumlahnya diperhitungkan kembali setiap minggunya. ...
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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