Judul: Aditif Penurun Emisi Gas Buang Kendaraan Bermotor dari Limbah Sel Darah Merah Sapi
Abstrak: Sebanyak 70% pencemaran udara disebabkan oleh emisi gas buang kendaraan bermotor. Gas buang akibat proses pembakaran tidak sempurna bersifat berbahaya dan beracun bagi makhluk hidup di antaranya karbon monoksida (CO), hidrokarbon (HC), dan nitrogen oksida (NOx). Tambahan aditif pendorong pembakaran (combustion booster) dapat membantu proses pembakaran. Penelitian ini memanfaatkan sel darah merah sapi sebagai aditif karena adanya hemoglobin yang dapat mengikat oksigen. Sel darah merah kering dalam metanol dengan konsentrasi 0.1% b/v dicampurkan dalam bensin sebagai bahan bakar. Kandungan komponen gas buang yang diukur ialah C, H, N, dan O. Sel darah merah dengan waktu maserasi selama 3 hari dapat menurunkan emisi gas buang CO, HC, dan NOx berturut-turut sebesar 71, 66, dan 67%. Kandungan oksigen dalam darah sapi dapat memperbaiki hasil pembakaran sehingga emisi yang dihasilkan berkurang.
Keyword: aditif, emisi, pembakaran, sel darah merah

Judul: Hemoglobin dari Limbah Darah Sapi sebagai Aditif Pembakaran pada Bensin: Penurun Emisi Gas Buang Kendaraan Bermotor.
Abstrak: Kendaraan bermotor menghasilkan 85% dari seluruh pencemar udara yang terjadi di Indonesia. Emisi kendaraan bermotor menghasilkan berbagai macam polutan. Hidrokarbon (HC) dan karbon monoksida (CO) merupakan senyawa gas beracun yang terbentuk akibat pembakaran yang tidak sempurna dalam proses kerja motor. Oksida nitrogen (NOx) terbentuk akibat suhu yang tinggi pada mesin kendaraan. Aditif ditambahkan pada bahan bakar untuk menghasilkan emisi gas buang yang ramah lingkungan pada kendaraan bermotor dengan cara meningkatkan konsumsi oksigen (O2). Oksigen banyak terdapat di dalam darah, terutama pada hemoglobin. Penelitian ini dilakukan untuk memanfaatkan hemoglobin dari limbah darah sapi sebagai aditif penurun emisi gas buang. Hemoglobin tersebut dicampurkan dengan metanol sebesar 0.1% (b/v) pada waktu maserasi 2, 3, dan 4 hari. Emisi gas HC, CO, NOx, dan CO2 menurun berturut-turut sebesar 61%, 72%, 78%, dan 35%. pada maserasi hari ke-3.
Keyword: aditif, emisi gas buang, hemoglobin

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