Judul: Kandungan merkuri (Hg) dalam air dan organ tubuh ikan petek (Leiognathus equulus) di perairan Ancol, Teluk Jakarta
Abstrak: Melalui proses bioakumulasi, logam berat merkuri (Hg) yang terkandung di dalam perairan akan mempengaruhi kelangsungan hidup biota laut termasuk ikan petek (Leiognathus equulus). Kadar merkuri dalam tubuh organisme dipengaruhi kondisi kualitas perairan. Penelitian ini mengkaji kandungan merkuri dalam air dan pada organ ikan petek serta respon histopatologisnya pada organ insang dan hati ikan petek. Penelitian dilaksanakan di Perairan Ancol, Teluk Jakarta pada bulan Oktober-Desember 2004. Pengambilan contoh air dan ikan masing-masing sebanyak 16 dan 3 stasiun. Hasil analisis kualitas air dibandingkan dengan baku mutu air laut untuk kehidupan biota laut (Keputusan Menteri Lingkungan Hidup No. 51 tahun 2004), kandungan merkuri pada organ tubuh ikan dibandingkan dengan kadar maksimum merkuri dalam tubuh ikan menurut Palar (2004) yaitu 0,23-0,8 ppm dan berbagai negara sebesar 0,5 ppm (Hutagalung 1991). Kualitas perairan Ancol masih berada pada kisaran yang normal. Kandungan merkuri di perairan dan pada organ ikan petek memiliki nilai yang sangat rendah karena terbatas pada sensitivitas alat AAS (Atomic Absorption Spectrophotometer). Hanya ikan yang tertangkap di stasiun 1 yang pada organ insang dan hati terdeteksi mengandung merkuri, tetapi nilai tersebut masih berada pada batas normal. Histopatologis yang tampak pada organ insang ikan petek tidak mengalami kelainan, sedangkan organ hati mengalami peradangan berupa nekrosis. Hal ini diduga merupakan tekanan fisiologis, dan reaksi penting dalam sistem pertahanan hidupnya.
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Judul: Kandungan timah hitam (Pb) dan Kadmium (Cd) dalam air, sedimen dan bioakumulasi serta respon histopatologis organ ikan alu-alu, Sphyraena barracuda di perairan teluk Jakarta
Abstrak: Logam berat yang masuk ke perairan akan terakumulasi baik di badan perairan, sedimen, maupun di dalam tubuh organisme yang hidup pada perairan yang sudah terkontaminasi. Semakin lama suatu perairan tercemar oleh suatu substansi beracun, maka dapat dipastikan keberadaannya membahayakan kondisi biota lingkungan perairan. Penelitian dilaksanakan pada bulan Oktober hingga Desember 2004 pada perairan Ancol, Teluk Jakarta. Lokasi pengambilan contoh dibagi menjadi 4 stasiun, dengan stasiun 1 terletak di sebelah barat dan stasiun 4 di sebelah timur. Pengambilan contoh air dan sedimen dilakukan pada stasiun 1, 2, 3, dan 4, sedangkan contoh ikan alu-alu (Sphyraena barracuda) diambil pada stasiun 1, 3, dan 4. Data primer berupa kandungan logam Pb dan Cd pada ikan didapatkan setelah dilakukan analisa dengan menggunakan metode Atomic Absorption Spectrophotometry (AAS) pada laboratorium terpadu FKH-IPB serta data histologi organ dalam ikan yang dilakukan analisanya di Balai Penelitian dan Veteriner Bogor.
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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