Judul: Kinerja Membran Komposit Kitosan-Karaginan pada Sistem Microbial Fuel Cell dalam Menghasilkan Biolistrik dari Limbah Pemindangan Ikan
Abstrak: Microbial fuel cell (MFC) merupakan suatu teknologi yang memanfaatkan mikroba sebagai katalis untuk mendegradasi bahan organik dan anorganik menjadi energi listrik. MFC dua bejana umumnya menggunakan membran penukar proton yang berfungsi untuk mengalirkan ion yang dihasilkan dari anoda ke katoda. Membran komposit kitosan-karaginan dapat digunakan sebagai alternatif dari membran penukar proton pada MFC. Penelitian ini bertujuan menganalisis karakteristik membran komposit kitosan-karaginan sebagai membran penukar proton pada MFC, menentukan kinerja MFC dalam menghasilkan elektrisitas, serta menentukan kinerja penurunan beban polutan limbah cair pada MFC. Membran komposit kitosan-karaginan dibuat dengan perlakuan perbedaan komposisi kitosan dan karaginan 1:1, 1.5:1, 3:1 (v/v). Membran komposit kitosankaraginan dengan perbandingan 1:1 menghasilkan nilai konduktivitas proton tertinggi sebesar 1.15 x 10-3 S/cm, kuat tarik tertinggi 7.047 MPa, tegangan listrik tertinggi sebesar 0.97 V, kuat arus tertinggi sebesar 7.02 mA, serta daya listrik tertinggi sebesar 6.84 mW. Nilai COD, BOD, serta TAN mengalami penurunan sebesar 90%, 76%,dan 32%.
Keyword: membran kitosan karaginan, microbial fuel cell, nilai elektrisitas

Judul: Imobilisasi Bacillus subtilis dengan Karaginan pada Anoda Microbial Fuel Cell Menggunakan Limbah Pemindangan Ikan
Abstrak: Limbah cair perikanan merupakan jenis limbah yang dapat menimbulkan pencemaran lingkungan. Pengolahan limbah cair perikanan dapat dilakukan melalui microbial fuel cell (MFC) untuk menghasilkan energi listrik dengan memanfaatkan mikroorganisme. Bacillus subtilis termasuk bakteri yang dapat menguraikan bahan organik dan mentransfer elektron untuk menghasilkan listrik. Penelitian ini bertujuan menentukan konsentrasi karaginan (0%, 1%, 1.5%, dan 2%) di anoda terhadap energi listrik dan beban polutan limbah cair ikan pindang setelah lima hari proses MFC. Biofilm bakteri yang terbentuk pada anoda setelah proses MFC mengalami peningkatan dan tertinggi pada konsentrasi karaginan 2% sebesar 6.3x10 cfu/cm2. Nilai tegangan listrik yang dihasilkan mengalami fluktuasi dan tertinggi sebesar 0.39±0.05aV pada karaginan 1%. Nilai chemichal oxygen demand (COD) dan biochemichal oxygen demand (BOD) limbah cair ikan pindang setelah MFC megalami penurunan sebesar 81.41% dan 60.02% pada karaginan 1.5%. Nilai pH pada limbah setelah proses MFC berkisar 7.11-7.33. Nilai total amonia nitrogen (TAN) mengalami peningkatan pada semua perlakuan dan tertinggi pada karaginan 0% sebesar 0.689±0.1 mg/L.
Keyword: energi listrik, limbah cair perikanan, microbial fuel cell

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