Judul: Karakteristik komunitas makrozoobenthos dan kaitannya dengan lingkungan perairan di Teluk Jakarta
Abstrak: Teluk Jakarta mempakan muara sungai yang membawa masukan bahan pencemar dari daratan. Akibatnya tejadi penurunan karakteristik perairan Teluk Jakarta. P e n m kualitas kolom perairan diduga juga menurunkan kualitas lingkungan sedimen, sehingga organisme benthik pun akan menyesuaikan diri dengan kondisi lingkungannya. Penelitian bertujuan untuk (1) Mendeskripsikan komunitas makrozoobenthos yang ditemukan di lokasi penelitian, (2) Mengidentifikasi kualitas lingkungan perairan di Teluk Jakarta, (3) Mengkaji keterkaitan antara zonasi status trofik yang ditemukan oleh Damar (2003) terbhp makrozoobenthos dan (4) Mengkaji hubungan antara makrozoobenthos dengan lingkungan perairan.
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Judul: Struktur komunitas Makrozobenthos di perairan teluk Jakarta dan teluk Banten
Abstrak: Penelitian ini bertujuan untuk mengetahui struktur komunitas makrozoobenthos dan melihat hubungannya dengan kondisi perairan serta membandingkan tampilan struktur komunitas makrozoobenthos antara perairan Teluk Jakarta dan Teluk Banten yang diduga tingkat pencemaran perairannya berbeda. Pene1itian ini dilaksanakan pada bulan Juli sampai November 2000 di perairan Teluk Jakarta dan Teluk Banten. Pengambilan contoh air dan makrozoobenthos dilakukan pada bulan Juli, September dan November 2000 pada tiga stasiun pengamatan di kedua lokasi tersebut berselisih waktu satu minggu. Pengamatan kualitas air dan makrozoobenthos dilakukan secara in situ dan ex situ. Analisis ex situ untuk contoh air dilakukan di Laboratorium Polution Control Biotrop dan Laboratorium Balai Besar Penelitian dan Pengembangan Industri Basil Pertanian (BBPPIHP) Bogor, dan identifikasi makrozoobenthos dilakukan di Laboratorium Manajemen Sumberdaya Perairan FPIK-IPB. Analisis substrat dilakukan di Pusat Penelitian dan Pengembangan Tanah dan Agroklimat Bogor. Komposisi jenis makrozoobenthos di perairan Bojonegara, Teluk Banten, terdiri atas lima kelas yaitu Gastropoda, Pelecypoda, Scapopoda, Echinodermata dan Polychaeta, sedangkan di perairan Muara Kamal, Teluk Jakarta, juga terdiri atas lima kelas dengan kelas-kelas yang agak berbeda yaitu Gastropoda, Pelecypoda, Scapopoda, Polychaeta, dan Crustacea. Perairan Bojonegara, Teluk Banten, mempunyai jumlah taksa makrozoobenthos lebih banyak, komposisi lebih tinggi, kepadatan lebih rendah, indeks keapekaragaman lebih tinggi, indeks keseragaman lebih tinggi, dan indeks dominansi lebih rendah bila dibandingkan dengan perairan Muara Kamal, Teluk Jakarta. Basil perhitungan indeks keanekaragaman, keseragaman, dan dominansi serta jenis dan kepadatan makrozoobenthos yang diuji dengan menggunakan statistik nonparametrik The Mann-Whitney V-test di kedua perairan yaitu Bojonegara, Teluk Banten dan Muara Kamal, Teluk Jakarta, menunjukkan bahwa secara umum jenis makrozoobenthos hampir sarna di kedua.perairantersebut{I'.<.0,0:;),...
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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