Judul: Potensi Deposisi Asam di Wilayah Industri Kabupaten Tangerang dan Sekitarnya Menggunakan Chimere Model
Abstrak: Increased industrial activity and transportation become the sources of pollutanand potentially degrade ambient air quality. One of the effect of air quality degradation is acid rain phenomenon. Purpose of this study is to determine the concentration of air pollutants (SO2 and NO2)and its potential for acid deposition. Tangerang is one of the cities that has the potential to decrease air quality due to the many industrial areas as well as the density of the motor vehicle. Potential acid deposition in this region were analyzed using Chimere program model. The results showed concentrations of SO2 maximum is27.50 ugm-3and NO2 gases maximum is 60ugm-3. This value is far below the Indonesian National Air Quality Standard of 60 ugm-3for SO2 and 100 ugm-3for NO2 in 365ugm-3. The average of NO2dry deposition fluxes in the five observation days is 5.3525x1015 molecule cm-2 s-1 and SO2 is 3.7573 x1015 molecule cm-2 s-1. While the average value of NO2wet deposition fluxes is 0 molecule cm-2 s-1 and SO2 is 4.8553x1010moleculecm-2 s-1. Estimated pH of rain on average in the study area is4.6, that indicate acid rain.
Keyword: Bogor Agricultural University (IPB), Acid rain, pH, NO2, SO2, Acid Deposition, Chimere Model

Judul: Potensi Pajanan Sulfur Dioksida (SO2) pada Masyarakat di Sekitar Wilayah Industri di Musim Kemarau.
Abstrak: As an industrial area, Tangerang district has high upper respiratory tract infection (URI or URTI) and tuberculosis cases. Sulfur dioxide (SO2) is one of industrial waste gases causing those diseases, the concentration tends to increase along dry season. By employing a model, a research related to the air pollutant exposure and public health can be more practical and efficient. This study aims to analyze the exposure of SO2 to the residents around Tangerang district in dry season including distribution of SO2 concentration emitted using Chimere model, the mass of pollutants inhaled by individual, Inhalation Transfer Factor (ITF), and Population Inhalation Transfer Factor (PITF). Results of Chimere model showed that SO2 concentration along dry season reached 0.2-2.7 μg m-3 and the mass of pollutants inhaled by individual ranging from 0.16–21 μg hr-1. ITF and PITF estimations for PT X in unstable atmospheric condition (A) were 2.7x10-8 and 9x10-5 respectively, whereas 1.2x10-9 and 4x10-5 in stable atmospheric condition (F) respectively. ITF and PITF estimations for PT Y in unstable atmospheric condition (A) were 4x10-8 and 1.3x10-4, whereas in stable atmospheric condition (F) were 1.9x10-8 and 6.5x10-4 respectively. Those PITF values were categorized as urban ambient air ranging from ~10-6- ~10-3.
Keyword: Bogor Agricultural University (IPB), PITF and SO2., Concentration, ITF, Industry

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