Judul: Penghilangan Gas H2S dan NH3 dengan Teknik Biofilter pada Ruang Produksi Pabrik Karet PTPN VIII Cikumpay, Purwakarta
Abstrak: Karet alam (natural rubber) yang diperoleh dari tanaman Hevea Braziliensis merupakan salah satu komoditas ekspor unggulan Indonesia yang jumlahnya cukup besar. Perkembangan industri karet menyebabkan timbulnya emisi berupa amoniak (NH3) dan hidrogen sulfida (H2S) yang mengganggu kesehatan pekerja pabrik karet. Biofilter adalah teknologi pengendalian pencemaran udara dengan cara menumbuhkan mikroorganisme pada bahan pengisi sehingga terbentuk biofilm atau biolayer yang dapat mengurai bahan-bahan pencemar udara.
Keyword: 

Judul: Penghilangan Polutan Gas Amoniak Dengan Biofilter Skala Pilot. Studi Kasus : PTPN VIII Pabrik Karet Cikumpay, Purwakarta, Jawa Barat.
Abstrak: Ammonia gaseous has a great potential to be a pollutant in rubber processing industries. The largest source of pollutant is at condensed latex processing. Therefore, biofilter was applied to remove emission of ammonia gaseous from the process. The objectives of this research were (1) to determine the level of physical and chemical absorption to ammonia sourness in forest soil based packing-bed material mixed with compost and carbonized rice husk, and (2) to determine the performance of biofilter to remove ammonia gaseous at pilot scale. Packing bed materials used were forest soil, bokashi compost and carbonized rice husk. The treatment of K111 was packed with forest soil, bokashi compost and carbonized rice husk with composised by 1:1:1. K112 was 1:1:2, K121 was 1:2:1, and K122 was 1:2:2. Physical and chemical characteristics of packing-bed materials within 5 parameters were obtained at ranked level of K112>K122>K111>K121. Carbonized rice husk influenced on physical - chemical characteristics of packing-bed materials, such as density, total porosity, water filled pore space and maximum capacity of loading, except air filled pore space. Those compositions were applied as packing bed materials to biofilters. Composition treatment of K111, K112, K121, K122 were applied to packing bed materials of B111, B112, B121 and B122. Biofilter were applied on pilot scale with 250 liters volume. For 55 days operation, ammonia pollutant concentrations were fluctuated from 0.18 to 68.66 ppm. Parameters inlet and outlet ammonia concentration, physical and chemical characteristics, population of ammonia oxidation bacteria and biofiltration process were monitored. Maximum removal capacities of B111, B112, B121 and B122 biofilters were 0.97, 1.46, 1.03 and 1.62 g-N/kg dry media. The result of biofiltration performance for 11 parameters showed that B112>B122>B121>B111. The best biofilter was determined by its ability in removing pollutant and the cost of required packing-bed. The price of packing bed materials from the lower prices were B111<B112<B122<B121. The cheapest price of packing bed material was B111, but it wasn’t a good performance of biofiltration. Therefore, B112 which was filled by forest soil, bokashi compost and carbonized rice husk with composition 1:1:2 was the best biofilter performance among them.
Keyword: 

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