Judul: Karakterisasi Particulate Matter (Pm) Dengan Teknik Analisis Nuklir Dan Strategi Pengendaliannya Studi Kasus Kota Tangerang Selatan.
Abstrak: Kota Tangerang Selatan merupakan wilayah yang mempunyai batas administrasi dengan DKI Jakarta, Bogor dan Kabupaten Tangerang,sebagai daerah yang berbatasan langsung dengan Ibukota DKI Jakarta menjadi dasarfokus utama pembagunan Kota Tangerang Selatan adalah di sektor pemukiman. Ketersediaan lahan pemukiman serta fasilitasnya serta semakin sempit dan mahalnya harga lahan di DKI Jakarta menjadi pendorong bagi masyarakat untuk bertempat tinggal di kawasan ini.Peningkatan pertumbuhan penduduk di Kota Tangerang Selatanmenyebabkan terjadinyapeningkatan aktivitas kegiatan manusiaseperti transportasi, industri pemukiman dan lain-lain hingga kemudian dapat mempengaruhi kualitas lingkungan udara.Penurunan kualitas udara tidak hanya berasal dari sumber pencemar lokal namun dapat berasal dari suatu lokasi yang jauh melintasi batas provinsi maupun negara karena dipengaruhi oleh faktor meteorologi yaitu arah dan kecepatan angin.Salah satu komponen udara yang dikaji dalam penelitian ini adalah paramater Particulate Matter (PM)yangmemiliki dampak paling berbahaya bagi kesehatan manusia karena kemampuannya untuk menembus sistem pernapasan manusia yang paling dalam. Sampel partikulat (PM2.5 dan PM2.5-10) dikumpulkan di kawasan PUSPIPTEK dengan pencuplik udara Gent Stacked Filter Unit (Gent SFU Sampler), periode Januari 2011 sampai Desember 2013, sebanyak 3 s.d 5 kali dalam sebulan dengan laju alir 18 liter/menit selama 24 jam. Identifikasi multi unsur dianalisis dengan spektroskopi X-Ray Fluorescence (XRF), karakterisasi sumber pencemar dilakukan dengan reseptor model Positif Matriks Factorization (PMF), estimasi lokasi sumber pencemar lokal dengan metode Conditional Probability Function (CPF) sedangkan perkiraan sumber pencemar dari lokasi jauh dilakukan menggunakan model dispersi waktu mundurHybrid Single ParticleLagrangian Integrated Trajectory(HYSPLIT) Hasil menunjukkan rentang rata-rata konsentrasi massa PM2.5 adalah 12.63 ± 1.60 - 15.89 ± 1.70 μg/m3 sedangkan untuk PM10 berkisar 29.00 ± 3.96 - 31.04 ± 3.28 μg/m3. Multi unsur yang teridentifikasi dengan XRF adalah Al, Ca, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, S, Si, Ti, V dan Zn. Karakterisasi partikulat halus (PM2.5) teridentifikasi 5 faktor yaitu industri peleburan logam Pb (9.61%), debu tanah (17%), campuran industri peleburan logam dan garam laut (13.02%), transportasi (44.36%) serta pembakaran biomassa (22.58%).Profil sumber pencemar PM2.5-10 menghasilkan 5 faktor yaitu Industri peleburan logam (6.48%), campuran industri dan debu tanah (19.04), Campuran garam laut dan biomassa (3.67%), Debu tanah (45.19%), dan transportasi (25.60%). Estimasi lokasi sumber pencemar lokal dengan metode CPFmenunjukkan bahwa salah satu faktor sumber pencemar yaitu sektor industri peleburan logam Pb berasal dari arah barat bagian luar wilayah administrasi Kota Tangerang Selatan sedangkan.Hasil Analisis sumber pencemar lokasi jauh dengan pemodelanHYSPLIT menunjukkan bahwa terdapat kontribusi kebakaran hutan Negara bagian utara Australia terhadap konsentrasi PM di Kota Tangerang Selatan.Pengendalian pencemaran PM di Kota Tangerang Selatan dapat dilakukan dengan mempertimbangkan tiga hal utama yaitu lingkungan, keteknikan yang ramah lingkungan dan ekonomi.
Keyword: Pencemaran udara, XRF Spektroskopi, karekterisasi sumber pencemar, Conditional probability function (CPF)

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