Judul: Studi Kemunduran Mutu Teripang pada Beberapa Macam Cara Penyimpanan
Abstrak: Penelitian ini bertujuan untuk mempelajari pengaruh pengolahan dengan cara perebusan dan mempelajari pengaruh cara penyimpanan pada suhu kamar, suhu dingin dan suhu beku terhadap kemunduran mutu teripang. Perlakuan yang diberikan terhadap penelitian ini adalah jenis teripang yang terdiri dari dua taraf, yaitu teripang pasir (A1), dan teripang batu (A2). Perlakuan pengolahan yang terdiri dari dua taraf, yaitu dengan perebusan (B₁) dan tanpa perebusan (B2). Perlakuan lama penyimpanan: untuk suhu kamar terdiri dari enam taraf yaitu 0 jam (C1), 3 jam (C2), 6 jam (C3), 9 jam (C4), 12 jam (C5) dan 15 jam (C6); untuk suhu dingin terdiri dari enam taraf, yaitu 0 hari (C₁), 3 hari (C2), 6 hari (C3), 9 hari (C4), 12 hari (C5) dan 15 hari (C6); untuk suhu beku terdiri dari tiga taraf, yaitu o bulan (C₁), 1 bulan (C2), 2 bulan (C3). Rancangan percobaan yang digunakan adalah Rancangan Acak Lengkap (RAL) pola Faktorial 2 x X 6 (untuk suhu kamar dan suhu dingin), 2 x 2 x 3 2 (untuk suhu beku) dengan ulangan masing-masing dua kali. ...
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Judul: Kemunduran Mutu Ikan Patin (Pangasius hypophthalmus) Segar Selama Penyimpanan pada Suhu Ruang
Abstrak: Tujuan umum dari penelitian ini adalah untuk mempelajari beberapa metoda pengukuran mutu ikan serta perubahan-perubahan yang terjadi selama proses kemunduran mutu ikan patin (Pangasius hypophthalmus) segar yang disimpan pada suhu ruang. Penelitian ini terdiri dari penelitian pendahuluan dan penelitian utama. Penelitian utama dilakukan berdasarkan penelitian pendahuluan.
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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