Judul: Pengaruh Hidrokarbon sebagai Sumber Karbon terhadap Prtumbuhan, Produksi Rhamnolipida serta Aktivitas Degradasi Hidrokarbon Oleh Pseudomonas earuginosa
Abstrak: Minyak bumi merupakan sumber pencemar lingkungan hidup yang berbahaya baik secara langsung maupun tidak langsung. Pencemaran lingkungan perairan akibat buangan atau limbah minyak bumi dapat menyebabkan terjadinya gangguan ekosistem pada lingkungan serta merupakan bahaya besar bagi flora dan fauna. Kondisi ini terjadi karena minyak bumi mengandung 50 - 90 persen hidrokarbon yang bersifat toksik dan dalam beberapa kasus bersifat karsinogenik, baik terhadap hewan maupun manusia. Salah satu metode penanganan pencemaran minyak bumi yang akhir-akhir ini dikembangkan adalah metode biologis dengan memanfaatkan mikroba untuk mendegradasi minyak bumi yang mencemari lingkungan (bioremediasi). Penguraian hidrokarbon oleh mikroba ini merupakan metode alternatif yang aman, sebab polutan dapat diuraikan menjadi bahan-bahan yang tidak berbahaya seperti CO2 dan H20. Metode biologis yang digunakan selain dapat mendegradasi cemaran minyak bumi, juga dapat dimanfaatkan untuk menghasilkan biosurfaktan.
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Judul: Pengaruh Komposisi Amonium dan Phospat terhadap Degradasi Minyak Bumi oleh Bakteri Pseudomonas aeruginosa dan Arthrobacter simplex
Abstrak: Sekitar 6.1 juta ton minyak bumi dan produknya terbuang ke taut setiap tahunnya akibat dari penambangan, pemumian minyak dan kegiatan lainnya, sehingga dapat merusak ekosistem (Godfiey, 1986). Selama ini penanganan masalah tersebut umumnya dilakukan secara fisik dan kimia yang dirasakan kurang efektif Hal ini mendorong perkembangan metode biologis yang memanfaatkan kemampuan mikroorganisme untuk menguraikan polutan seperti hidrokarbon, yang dikenal dengan istilah bioremediasi.
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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