Judul: Uji Antagonisme antara Trichoderma spp. dan Fusarium oxysporum pada berbagai pH Medium In vitro
Abstrak: Penelitian ini bertujuan untuk menginventarisasi jenis-jenis Trichoderma di beberapa lokasi pertanaman buncis (Phaseolus vulgaris). Di samping itu juga untuk mengetahui isolat Trichoderma yang menyebabkan persentase hambatan tertinggi terhadap Fusarium oxysporum yang diisolasi dari tanaman buncis pada berbagai pH medium agar. Pengambilan contoh tanah dilakukan di daerah pertanaman yang sedang atau sehabis ditanami buncis. Pada setiap petak diambil 5 contoh tanah secara acak, kemudian dikeringudarakan dan disaring dengan saringan 9 mesh. Untuk mengisolasi Trichoderma dari contoh tanah digunakan metode pengenceran. Setelah itu setiap koloni Trichoderma yang berbeda warna dan pola pertumbuhan dari setiap pengenceran yang memberikan koloni cendawan secara terpisah dan dari setiap lokasi dimurnikan pada medium Potato Dextrose Agar (PDA). Kemudian untuk mengidentifikasi cendawan digunakan medium Malt Extract Agar (MEA). ...
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Judul: Identifikasi Molekuler Trichoderma spp. dan Aktivitas Antagonisnya terhadap Fusarium oxysporum f.sp cubense
Abstrak: Trichoderma is a cosmopolitan fungus that widespread in the soil and rotten wood. Various species of Trichoderma have been used, as a producer of enzymes and other metabolites or as a biocontrol agent. Therefore, identification of Trichoderma strains become important. Until now, many identity culture Trichoderma in Indonesia have doubtful validity. Trichoderma concept changed from time to time. Significant efforts in formulating the concept of Trichoderma was first performed by Rifai in 1969 based approach to morphological characters. Trichoderma species are divided into 9 aggregates, namely T. piluliferum, T. polysporum, T. hamatum, T. koningii, T. aureoviride, T. harzianum, T. longibrachiatum, T. pseudokoningii, and T. viride. This concept was later revised by Bissett in 1991 with the introduction of section 5 (section) is section Trichoderma, section Longibrachiatum, section Saturnisporum, section Pachybasium, and section Hypocreanum. Along with the development of methods of DNA extraction and phylogenetic analysis of the fungus, identification of Trichoderma species have been changed from morfohology in to combination of morphology and phylogenetic analysis. The first, phylogenetic analysis approach of Trichoderma by Kindermann in 1998 using DNA sequence analysis of the ITS region to 85 strains of Trichoderma. Then, when the approach of a single gene can not be relied upon to distinguish some species that are morphologically similar, multigene analysis was able to distinguish the species. At this time locus used in phylogenetic analysis and species identification in Trichoderma has been established that, ITS, tef α-1, RPB2, and Endokitinase. Trichoderma research in Indonesia have been carried out, but only a few research that include diversity and taxonomic studies. Trichoderma diversity and taxonomic studies from Indonesia is important because it can serve as a model for other fungal diversity studies. Therefore, this study aims to identify the 27 accession numbers Trichoderma collection of LIPIMC and IPBCC derived from litter and soil in Indonesia through a variety of phylogenetic approaches two genes (ITS and tef α-1). In addition, this research also aimed to characterize the antagonistic activity of Trichoderma spp. against Fusarium oxysporum f.sp. cubense (Foc), causes Fusarium wilt disease in bananas, because of the demands of the community to develop a biocontrol Foc agent. Identification of Trichoderma started early stages by performing morphological characterization. Colonies character observed in 5 days that were incubated at room conditions. After that, the microscopic preparations were made by using the method of Riddle and preparations were observed under a microscope Olympus BX53. Colony characters and microscopic characters are recorded as a material for morphological identification. Molecular phylogenetic analysis was started by growing Trichoderma isolates on media Potato Dextrose Broth (PDB) and incubated at 27˚C for 3-5 days. DNA extraction using PhythopureTM DNA Extraction Kit (GE Healthcare, UK), and then proceed with the ITS region amplification primer pair ITS4 (5'-TC CTCCGCTTATATATGC-3') and ITS5 (5'-GGAAGTAAAAGTCGTAACAA GG-3'), and α tef -1 (translation elongation factor-1 α) with primer tef1-fw (5'-GT GAGCGTGGTATCACCATCG-3 ') and tef1-rev (5'-GCCATCCTTGGAGACC AGC-3'). DNA sequencing is done by sending the product to FirstBASE (Malaysia). DNA sequences were analyzed by PCR using the MEGA program (Molecular Evolution and Genetic Analysis) version 5.05. On this analysis, kinship was
Keyword: biocontrol, Fusarium oxysporum f.sp cubense, phylogenetics, taxonomy, Trichoderma

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