Judul: Mempelajari Ekstraksi Oleoresin dan Karakteristik Mutu Oleoresin dari Bagian Cabe Rawit ( Capsicum frutescens L.)
Abstrak: Penelitian ini bertujuan untuk mempelajari ekstraksi dan karakteristik mutu oleoresin dari bagian cabe rawit. Analisis yang dilakukan meliputi laju ekstraksi dan karakteristik sifat mutu oleoresin yang dihasilkannya.
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Judul: Pengaruh tingkat kematangan dan ukuran bubuk cabe rawit (Capsicum frutescens L) serta perbandingan pelarut dengan bubuk cabe, terhadap rendemen dan mutu oleoresin yang dihasilkan
Abstrak: Cabe rawit (Capsicum frutescens L.) adalah salah satu spesies tanaman cabe (Capsicum sp.), yang merupakan hasil pertanian bersifat kamba dan mudah rusak (perishable). Persentase kerusakan cabe cukup tinggi, yaitu mencapai lebih 40 persen, disebabkan terutama oleh mikroorganisme diantaranya Bacterium anthomonas, Glasparium sp., Colletotricum capsici dan Anthomonas solanacearum. Selain itu perubahan fisiologis dalam cabe juga dapat menyebabkan kerusakan produk cabe. Keadaan ini menyebabkan tingginya nilai kehilangan (loss) cabe dan tidak dapat dijual. Mengingat sifat cabe yang mudah rusak dan di lain pihak produksi cabe di Indonesia terus meningkat, maka perlu dilakukan usaha pengolahan cabe menjadi bentuk yang lebih awet dan mengurangi sifat kambanya. adalah pengolahan cabe menjadi oleoresin. Salah satu usaha itu Tujuan penelitian ini adalah untuk mempelajari pengaruh. tingkat kematangan buah cabe rawit, ukuran bubuk cabe rawit kering dan perbandingan pelarut dengan bubuk cabe terhadap rendemen dan mutu oleoresin cabe rawit yang dihasilkan. Perlakuan yang digunakan terdiri dari tiga faktor, yaitu tingkat kematangan cabe (A) dengan tiga taraf; matang (mature), masak (ripe) dan lewat masak (over ripe); ukuran bubuk cabe kering (B); 40 mesh dan 60 mesh dan perbandingan pelarut dengan bubuk cabe (C); 5 : 1,6 : 1, dan 7: 1. Menggunakan Rancangan Acak Lengkap Faktorial dengan dua kali ulangan. ...
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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