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: Analisis Tingkat Pencemaran Udara berdasarkan Musim di Kota Tangerang.
Abstrak: Pencemaran udara dikenal sebagai masalah lingkungan yang terasosiasi dengan wilayah perkotaan karena semakin meningkatnya pertumbuhan penduduk dan penggunaan bahan bakar yang digunakan untuk transportasi dan industri. Kota Tangerang adalah salah satu kota metropolitan di Provinsi Banten yang padat penduduk. Tujuan penelitian ini untuk menganalisis tingkat pencemaran udara di Kota Tangerang dengan menghitung nilai Indeks Standar Pencemaran Udara (ISPU) dari data pengukuran kualitas udara, membandingkan ke dalam wilayah peruntukan, dibandingkan saat musim kemarau dan musim hujan, serta uji korelasi unsur-unsur meteorologi dengan nilai ISPU. Data yang digunakan berupa data sekunder, yaitu data konsentrasi udara ambien dan data meteorologi dari tahun 2013 sampai 2016. Data konsentrasi udara ambien terdiri dari NO2, SO2, O3, CO, dan PM10. Data meteorologi terdiri dari suhu rata-rata, kelembaban rata-rata dan kecepatan angin rata-rata. Hasil ISPU dipetakan menggunakan Geographic Information System (GIS). Uji korelasi menggunakan Minitab. Hasil penelitian ini menunujukkan ISPU di Kota Tangerang dari tahun 2013 sampai 2016 berada pada tingkat pencemaran udara yang relatif sedang. Parameter konsentrasi pencemar tertinggi didominasi oleh PM10. ISPU tertinggi berada di wilayah industri. Hasil perbandingan rata-rata ISPU pada saat musim hujan lebih tinggi daripada musim kemarau. Hasil korelasi data meteorologi dengan nilai ISPU bernilai positif pada parameter kelembaban rata-rata dan bernilai negatif pada parameter suhu rata-rata dan kecepatan angin rata-rata. Nilai korelasi tertinggi yaitu pada parameter kelembaban rata-rata.
Keyword: ISPU, Kota Tangerang, musim hujan, musim kemarau, pencemaran udara

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