Judul: Potensi Ecoenzyme Fuel Cell Berbasis Kulit Jeruk Keprok (Citrus reticulata) sebagai Energi Baru Terbarukan
Abstrak: Biomassa merubah sistem energi menuju modernisasi sistem bioenergi dengan memanfaatkan konsep biochemical system (BESs). Arah pergerakan energi terbarukan memanfaatkan katabolisme komponen organik substrat secara mikrobial dan oksidasi mikroorganisme yang menghasilkan elektron dan proton yang dikenal dengan microbial fuel cell (MFC). Seiiring dengan perkembangan jaman telah dikembangkan subtrat MFC yaitu fermented substrate. Salah satu fermented substrate yang dapat dikembangkan adalah ecoenzyme. Penelitian ini bertujuan menentukan energi listrik dari larutan Ecoenzyme Citrus reticulata dan elektrolit kalium permanganat (KMnO4). Ecoenzyme yang dibuat bedasarkan perbandingan 3:1:10 yang terdiri dari limbah kulit jeruk, molase dan air. Variabel yang diuji berupa tegangan, kuat arus dan rapat daya yang dihasilkan dari rancangan ecoenzyme fuel cell. Variasi perlakuan sistem dibedakan menjadi dua yaitu jumlah elektroda dan pengadukan serta tanpa pengandukan. Pengujian kinerja sistem diamati selama 4 minggu (600 jam). Tegangan tertinggi yang dihasilkan yaitu sebesar 650 mV. Arus terbesar yang dihasilkan oleh rangkaian adalah 29.55 mA. Penggunaan ecoenzyme dan KMnO4 dapat menghasilkan rapat daya (power density) terbesar 750 W/m2 pada perlakuan 3 pasang elektroda pada kecepatan 200 rpm.
Keyword: biomassa, Citrus reticulata, ecoenzyme, energi terbarukan, microbial fuel cell

Judul: Penggunaan Limbah Cair yang Berbeda pada Sistem Microbial Fuel Cell (MFC) sebagai Penghasil Biolistrik
Abstrak: Limbah cair dari industri perikanan, pengolahan kelapa sawit, dan tekstil berpotensi menghasilkan suatu energi listrik. Fuel cell sebagai energi alternatif dengan bebas polusi udara dan degradasi limbah. Salah satu sumber bahan baku fuel cell adalah mikroorganisme, energi listrik didapat melalui proses degradasi bahan organik oleh mikroorganisme melalui mekanisme bioelektrokimia atau dapat disebut dengan Microbial Fuel Cell (MFC). Tujuan penelitian ini yaitu mengidentifikasi potensi energi dari kandungan limbah cair perikanan, pengolahan kelapa sawit dan tekstil menggunakan teknologi microbial fuel cell dalam menghasilkan listrik serta membandingkan energi yang dihasilkan. Limbah cair perikanan, limbah cair tekstil dan limbah cair kelapa sawit dengan sistem MFC dapat menghasilkan tegangan listrik dengan rata-rata secara berurutan 0,33± 0,07 V, 0,28± 0,08 V dan 0,39± 0,08 V. Nilai tegangan tertinggi terdapat pada limbah cair kelapa sawit. Beban limbah yang dihasilkan setelah pelakuan pada analisis COD dan BOD masih diatas baku mutu limbah cair dari ketiga sampel.
Keyword: BOD, COD, limbah cair, microbial fuel cell, tegangan listrik

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