Judul: Pengaruh Kondisi Pemanasan Basah terhadap Penurunan Kadar Aflatoksin
Abstrak: Penelitian ini dilakukan untuk mempelajari pengaruh pemanasan basah, yaitu pengukusan (95˚ C), perebusan (100˚), dan pemanasan bertekanan (otoklaf, 121˚), terhadap kandungan aflatoksin. Pengukusan dilakukan pada suhu 95˚ C dengan interval waktu 25 menit (0-125 menit), perebusan pada suhu 100˚ C dengan selang waktu 10 menit (0-50 menit) dan pemanasan bertekanan pada suhu 121˚ C dengan selang waktu 5 menit (0-25 menit).
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Judul: Pengaruh lama penyimpanan bahan makanan ternak dengan penambahan asam pitat terhadap kandungan aflatoksin
Abstrak: Tujuan penelitian ini adalah untuk mempelajari penga- ruh macam bahan makanan, penambahan asam pitat dan lama penyimpanan terhadap kandungan aflatoksin B1, B2, G1, G2 dan aflatoksin total. Penelitian ini telah dilakukan di Jurusan Ilmu Nutrisi dan Makanan Ternak Fakultas Peternakan dan di Laboratorium Kimia terpadu Institut Pertanian Bogor. Bahan penelitian yaitu jagung kuning, bungkil kedelai, bungkil kelapa dan dedak halus yang merupakan bahan makanan yang lazim dipergunakan di Indonesia. Masing-masing bahan makanan diberi perlakuan yaitu penambahan asam pitat dengan taraf 0, 0.75, 1.25 dan 1.75 persen, kemudian dikemas didalam karung goni yang diisi masing- masing dua kilogram bahan makanan. Bahan makanan tersebut disimpan didalam gudang yang beralaskan palet selama empat waktu penyimpanan yaitu 3, 6, 9 dan 12 minggu, sedangkan yang untuk penyimpanan nol minggu langsung diambil untuk dianalisa. Rancangan acak lengkap dengan percobaan faktorial 4 X 4 X 5 dengan dua kali ulangan merupakan penuntun penelitian dengan rancangan perlakuan macam bahan makanan (A) terdiri dari jagung kuning (a1), bungkil kedelai (a2), bungkil kelapa (a3), dedak halus (a4). pitat (B) terdiri dari 0% (b1), 0.75% Penambahan asam (b2), 1.25% (b3) dan 1.75% (b4). Lama penyimpanan yaitu o minggu (c₁), 3 minggu (C₂), 6 minggu (C3), 9 minggu (C4) dan 12 minggu (C5). Hasil penelitian di analisis menggunakan sidik ragam yang dilanjutkan dengan uji Beda Nyata Jujur (BNJ) dan persamaan regresi. Pada penelitian ini kandungan aflatoksin G₂ tidak 2 terdeteksi, maka kandungan aflatoksin G₂ dianggap o ppb. Pengaruh macam bahan makanan terhadap rataan kandungan aflatoksin B₁ pada bahan makanan jagung kuning, bungkil kedelai, bungkil kelapa dan dedak halus masing-masing 23.93, 16.40, 21.63 dan 2.55 ppb, B₂ masing-masing adalah 2.89, 4.24, 5.98 dan 0 ppb, sedangkan Aflatoksin G₁ 1 masing-masing adalah 13.93, 15.80, 12.35 dan 2.65 ppb dan aflatoksin total masing-masing adalah 40.75, 36.44, 39.96 ..dst
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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