Judul: Isolasi dan Uji Antimikrob Metabolit Sekunder Ekstrak Kultur Jamur Endofit AFKR-5 dari Tumbuhan Akar Kuning (Arcangelisia flava (L) Merr)
Abstrak: Endophytic fungi associated with medicinal plant were known to produce bioactive secondary metabolites similar to its host plant. This study aimed to obtain bioactive secondary metabolites as antimicrobial from endophytic fungi AFKR-5 associated with akar kuning plant from Bogor Botanical Garden. Methanol fraction of ethyl acetate extract from AFKR-5 culture in potato dextrose broth medium was capable to produce the bioactive secondary metabolites F3.4 (10.375 mg/L) with retention factor of 0.30. Antimicrobial activity test using Kirby-Bauer disc diffusion method showed that F3.4 was a broad antimicrobial spectrum on 3 pathogenic microbes, namely Gram-positive bacteria Staphylococcus aureus, Gram-negative bacteria Escherichia coli, and Candida albicans mold. The determination of minimum inhibitory concentration (MIC) and the minimum fungicidal concentration (MFC) were carried out with liquid microdilution method. F3.4 was the most potential with MIC value of 16 μg/mL against C. albicans, twice as stronger than the commercial antifungal Nystatin which was only fungistatic with MIC value of 32 μg/mL. F3.4 was fungicidal with MFC value of 32 μg/mL against C. albicans, so that it is potential to be developed as antimicrobial, especially antifungal.
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Judul: Isolasi dan Uji Aktivitas Antibakteri dari Senyawa Bioaktif Jamur Endofit 20DnSi-1.
Abstrak: Peningkatan jumlah prevalensi resistensi terhadap antibiotik mendorong para ahli untuk terus mencari berbagai sumber senyawa aktif untuk menemukan senyawa antibiotik atau antibakteri baru yang dapat mengobati penyakit infeksi. Jamur endofit 20DnSi-1 merupakan salah satu isolat jamur endofit yang berasosiasi dengan daun Waru (Hibiscus tiliaceus L.). Jamur ini memiliki potensi untuk dikembangkan sebagai sumber antibakteri baru. Penelitian ini bertujuan mengisolasi senyawa bioaktif dari jamur endofit 20DnSi-1 dengan metode kromatografi kolom, dan menguji aktivitas antibakterinya dengan menentukan nilai MIC (Minimum Inhibitory Concentration). Serangkaian proses isolasi yang telah dilakukan menghasilkan tiga fraksi murni yaitu FD1.7/FD1.8.3, FD1.8.1.1, dan FD1.8.2/FD1.8.1.4 dengan kapasitas produksi masing-masing sebesar 11.05 mg/L, 2.63 mg/L dan 3.99 mg/L. Ketiga fraksi tersebut tidak memiliki aktivitas antibakteri yang kuat terhadap Escherichia coli Ina-CC B5. Fraksi FD1.7/FD1.8.3 memiliki aktivitas antibakteri yang kuat terhadap Staphylococcus aureus Ina-CC B4 dengan nilai MIC sebesar 32 μg/mL, mendekati nilai MIC kloramfenikol (4 μg/mL), sedangkan untuk FD1.8.1.1 dan FD1.8.2/FD1.8.1.4 tidak begitu kuat dengan nilai MIC diatas 256 μg/mL. Dari ketiga fraksi tersebut, FD1.7/FD1.8.3 memiliki potensi untuk dikaji lebih lanjut sebagai antibiotik karena memiliki aktivitas antibakteri yang kuat terhadap bakteri Gram positif.
Keyword: Antibakteri, Isolasi, Jamur Endofit 20DnSi-1, Kromatografi, MIC

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