Judul: Karakterisasi Gugus Fungsi dan Aplikasi Biosurfaktan Bacillus subtilis ATCC 19659 dengan Media Limbah Tahu
Abstrak: Biosurfaktan merupakan metabolit sekunder yang diproduksi oleh Bacillus subtilis. Biosurfaktan memiliki kemampuan emulsifikasi dan menurunkan tegangan permukaan. Biosurfaktan yang diproduksi oleh B.subtilis adalah lipopeptida. Oleh karena itu, biosurfaktan dapat digunakan pada microbial enhaced oil recovery (MEOR). Pada penelitian ini, biosurfaktan dari B.subtilis ATCC 19659 dikarakterisasi. Produksi biosurfaktan menggunakan Nutrient Broth (NB) dan limbah cair tahu. Penentuan struktur dan kandungan senyawa dilakukan dengan spektroskopi FTIR dan GC-MS. Analisis FTIR menunjukkan biosurfaktan memiliki gugus fungsi O-H, CH3, C=O, dan N=O. Hasil analisis GC-MS menemukan senyawa yang berperan sebagai pengemulsi yaitu 1-oktadekanol dari media limbah cair tahu dan asam oktadekenoat dari media NB. Berdasar uji aplikasi biosurfaktan mengeluarkan minyak mentah dari pasir, biosurfaktan dari media NB dapat mengeluarkan 2 mL, sedangkan biosurfaktan dari media limbah cair tahu dapat mengeluarkan 3.67 mL.
Keyword: Bacillus subtilis, biosurfaktan, FTIR, GC-MS, uji aplikasi

Judul: Biosurfactant Production of Bacillus subtilis from Tofu Liquid Waste
Abstrak: Biosurfaktan merupakan substansi permukaan aktif yang diproduksi oleh mikroorganisme dan telah diterapkan dalam metode microbial enhanced oil recovery. Biosurfaktan kurang ekonomis jika diproduksi dalam skala besar karena biaya produksi yang tinggi. Alternatif yang digunakan untuk menekan biaya produksi dalam penelitian ini digunakan limbah organik sebagai media tumbuh, yaitu tahu cair. Tujuan penelitian ini adalah memperoleh biosurfaktan yang diproduksi Bacillus subtilis ATCC 19659 menggunakan media limbah tahu cair serta mendapatkan kondisi optimum produksi meliputi tingkat salinitas dan pH media. Adanya biosurfaktan dibuktikan dengan uji emulsifikasi. Hasil penelitian menunjukkan bahwa densitas sel bakteri di limbah tahu cair (0.1622‒1.0594) lebih rendah dibandingkan di nutrient broth (0.0401‒3.0600). Produksi biosurfaktan optimum di media limbah tahu cair didapatkan pada jam ke-96. Waktu optimum tersebut lebih lama dibandingkan produksi di media nutrient broth, yaitu 72 jam. Indeks emulsifikasi di media limbah tahu cair juga lebih rendah (40.0 %) dibandingkan pada media kaldu nutrien (48.3 %). Indeks emulsifikasi produksi biosurfaktan tertinggi diperoleh pada tingkat salinitas media 1 % (w/v) dan rentang pH 6 ‒ 8.
Keyword: Bogor Agricultural University, Institut Pertanian Bogor, IPB, biosurfactant, Bacillus subtilis, tofu liquid waste, emulsification index, Indeks emulsifikasi, Pertumbuhan bacillus subtilis, Bakteri penghasil biosurfaktan

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