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: Sebaran Partikulat (PM10) pada Musim Kemarau di Kabupaten Tangerang dan Sekitarnya
Abstrak: Air pollution originating from anthropogenic activities nowadays be increased along with industry development and transportation sector. Tangerang District and its surrounding areas are a large industrial area in Indonesia, so people in the region have a high potential for exposure to air pollutant. Particulate is one of the pollutions. Monitoring the pollutant dispersion is necessary to be conducted as a consideration to take a policy related to environmental issues. However, pollutant monitoring constrained by cost and availability of tools in the field, so the model is used to make pollutant monitoring easier. This study aims to determine the direction of PM10 pollutant dispersion in Tangerang and the surrounding area based the dominant wind direction in the dry season. The method in this study uses Chimere model that can generate daily air pollution forecast. Result shows high PM10 concentration around Tangerang City. High number of industries and vehicles and also the dominant winds eastward in the dry season led to a concentration of PM10 in Tangerang City until South Tangerang City higher than around, namely reached 26-28 μg m-3. Correlation value between wind speed and particulate concentration is -0.46 indicated that the wind speed has considerable influence the level of PM10 concentrations.
Keyword: Bogor Agricultural University (IPB), Pollutant dispersion, Tangerang District, PM10

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