Judul: Kemampuan Penghambatan Streptomyces spp. terhadap Mikroba  Patogen Tular Tanah pada Beberapa Kondisi Pertumbuhan: Jenis  Media, Waktu Produksi, pH, dan Suhu.
Abstrak: Pemanfaatan agen biokontrol sebagai alternatif pengendalian terhadap mikroba patogen semakin berkembang. Hal ini dipicu oleh semakin banyaknya pencemaran yang disebabkan oleh pestisida yang terakumulasi di tanah. Agen biokontrol terhadap penyakit tumbuhan mampu menurunkan populasi pathogen penyebab penyakit atau semua aktivitas yang dihasilkannya dalam menyerang tanaman. Namun dengan semakin banyaknya penyebab penyakit yang belum diketahui musuh alaminya, maka sekarang ini pemanfaatan mikroba sebagai agen biokontrol banyak diteliti. Mikroba tersebut dapat menghasilkan berbagai senyawa anti mikroba yang dapat dikembangkan untuk subtitusi senyawa kimia sintetik. Selain itu penggunaan senyawa anti mikroba yang dihasilkan oleh mikroba untuk mengontrol penyakit tanaman dapat diaplikasikan dalam konsentrasi yang rendah, mudah diuraikan oleh mikroba tanah, sehingga tidak terjadi akumulasi residu, serta tidak mengganggu keseimbangan lingkungan
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Judul: Test the Potential of Antagonistic Microbes as Biocontrollers of Plant Disease Causes
Abstrak: Pengendalian hayati merupakan alternatif untuk mengurangi penggunaan pestisida karena dapat menekan pertumbuhan patogen tanaman dan tidak berdampak negatif terhadap lingkungan. Tujuan penelitian ini yaitu menguji efektivitas mikrob antagonis sebagai pengendali hayati penyebab penyakit tanaman. Pada penelitian ini menggunakan tiga mikrob antagonis (Trichoderma sp., Gliocladium sp., Bacillus sp.) dan tiga cendawan patogen uji (Rhizoctonia sp., Sclerotium sp., Botryodiplodia sp.). Penelitian ini terbagi menjadi tiga percobaan, yaitu (1) uji antagonis secara in vitro, (2) uji metabolit sekunder dan (3) uji pertumbuhan pada benih sengon (in vivo). Parameter yang diukur berupa persentase penghambatan, daya kecambah, kecepatan tumbuh, kejadian penyakit, dan intensitas penyakit. Cendawan Trichoderma sp. dan Gliocladium sp. mampu menghambat pertumbuhan cendawan patogen Sclerotium sp., Rhizoctonia sp., dan Botryodiplodia sp. berkisar 61,82% hingga 80,00%. Metabolit sekunder cendawan Gliocladium sp. lebih mampu menghambat pertumbuhan miselia cendawan patogen Rhizoctonia sp., Sclerotium sp., dan Botryodiplodia sp. dibanding cendawan Trichoderma sp. dengan nilai penghambatan berturut-turut adalah 71,13%, 33,83%, 23,58%. Gliocladium sp., Trichoderma sp., dan Bacillus sp. mampu menekan kejadian penyakit (14,29%, 12,38%, dan 15,24%) dan intensitas serangan damping-off yang disebabkan Rhizoctonia sp. (8,91%, 8,57%, dan 9,43%). Hal ini ini menunjukkan agensia hayati berpotensi mengendalikan penyakit tanaman.
Keyword: agensia hayati, damping-off, metabolit sekunder, 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