Judul: Potensi Antibakteri Ekstrak Kunyit (Curcuma domestica), Daun Jambu Biji (Psidium guajava L.), Sirih (Piper betle L.) dan Sambiloto (Andrographis paniculata (Burm.f.) Nees) terhadap Bakteri Aeromonas hydrophila
Abstrak: Bakteri Aeromonas hydrophila merupakan penyebab penyakit bercak merah pada budidaya ikan air tawar. Penyakit tersebut biasanya dapat diobati dengan antibiotik. Tetapi penggunaan antibiotik secara terus-menerus dapat menyebabkan organisme penyebab penyakit tersebut resisten. Sebagai alternatif lain yang digunakan sebagai obat antibakteri adalah tanaman obat tradisional seperti kunyit, daun jambu biji, sirih dan sambiloto. Dengan adanya sifat antibakteri dalam tanaman obat tradisional tersebut maka penelitian ini bertujuan untuk mengetahui apakah ke-4 macam tanaman obat tradisional tersebut mempunyai potensi antibakteri terhadap bakteri Aeromonas hydrophila serta mengetahui Minimum Inhibitor Concentration (MIC) nya.
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Judul: Screening Herbal to Streptococcosis and Motile Aeromonad Septicaemia (MAS) by In Vitro.
Abstrak: Streptococcosis disebabkan oleh bakteri Streptococcus agalactiae, sedangkan penyakit motile aeromonad septicaemia (MAS) disebabkan oleh bakteri Aeromonas hydrophila. Penelitian ini merupakan pengujian dasar untuk mengetahui potensi antibakteri dari 18 ekstrak tanaman, dengan cara ekstraksi menggunakan pelarut etanol 95% dan ddH2O. Hasil ekstraksi yang diperoleh berupa ekstrak kasar. Pengujian dilakukan secara in vitro yang terdiri dari uji sensitivitas bakteri terhadap ekstrak tanaman dan minimum inhibitory concentration (MIC). MIC dilakukan dengan metode macrodilution. Bakteri yang digunakan adalah S. agalactiae dan A. hydrophila. Dari 18 tanaman pada pengujian in vitro dari ekstraksi etanol 95% sebanyak 15 tanaman (83,0%) memiliki daya antibakteri terhadap S. agalactiae dan 4 tanaman (22,2%) terhadap bakteri A. hydrophila. Tanaman yang diekstraksi dengan pelarut ddH2O sebanyak 5 macam tanaman (27,7%) menunjukkan daya antibakteri terhadap S. agalactiae dan terhadap A. hydrophila tidak ada ekstrak tanaman yang menunjukkan zona hambat. Tanaman sirih (Piper betle) dan kimanila (Cassia alata), yang diekstraksi dengan etanol 95% ataupun ddH2O, keduanya memiliki daya antibakteri terhadap S. agalactiae. Nilai MIC pada ekstraksi etanol 95% terhadap S. agalactiae dari tanaman kunyit (Curcuma domestica), kunyit putih (Curcuma zedoaria), temulawak (Curcuma xanthorrhiza), sirih (P. betle), meniran (Phyllanthus niruri), ketapang (Terminalia catappa) adalah sebesar 25 mg/ml, dari tanaman jawer kotok (Plectranthus scutellaroides) adalah sebesar 12,5 mg/ml. Delapan jenis tanaman lainnya, yaitu bawang putih (Allium sativum), kirinyuh (Eupatorium inulaefolium), kimanila (C. alata), petai cina (Leucaena leucocephala), kipahit (Tithonia diversifolia), combrang (Etlingera elatior), babandotan (Ageratum conyzoides), jambu monyet (Anacardium occidentale) di atas 25 mg/ml. Pada pengujian terhadap A. hydrophila, tanaman sirih (P. betle), kimanila (C. alata), jawer kotok (P. scutellaroides), jambu monyet (A. occidentale) memiliki nilai MIC di atas 25 mg/ml. Nilai MIC tehadap S. agalactiae dari kelima tanaman uji, yaitu bawang putih (A. sativum), sirih (P. betle), kirinyuh (E. inulaefolium), kimanila (C. alata), kipahit (T. diversifolia) yang diekstraksi dengan ddH2O berada di atas 25 mg/ml.
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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