Judul: Evaluasi Tingkat Kontaminasi Cu, Zn, Pb dan Cd pada Lahan Sawah di Kota Tangerang Provinsi Banten
Abstrak: Kota Tangerang merupakan salah satu daerah penyangga Ibukota negara Indonesia yang dapat dikategorikan sebagai kota industri. Keberadaan industri selain berdampak positif bagi perekonomian daerah, juga dapat berdampak negatif bagi kualitas lingkungan. Berdampak positif karena dapat menyediakan lapangan pekerjaan dan meningkatkan pendapatan daerah. Berdampak negatif karena limbah industri dapat mencemari lingkungan, antara lain tanah pertanian yang berada di sekitarnya. Penelitian ini bertujuan mengukur konsentrasi dan tingkat kontaminasi logam berat Cu, Zn, Pb dan Cd pada tanah dan serapannya pada beras serta mendapatkan gambaran mengenai kualitas tanah pertanian di sekitar Kota Tangerang, sehingga dapat digunakan sebagai dasar penyusunan rekomendasi pengelolaan lahan pertanian berkaitan dengan pencemaran logam berat. Penelitian ini dimulai dengan inventarisasi lahan-lahan sawah di Kota Tangerang, kemudian dilakukan pengambilan contoh tanah dan beras di 13 kelurahan yang masih memiliki potensi budidaya padi sawah. Analisis tekstur, pH, C-organik dan KTK tanah, serta konsentrasi total logam berat Cu, Zn, Pb dan Cd dalam tanah dan beras (metode destruksi basah dengan HNO3 dan HClO4 Hasil penelitian menunjukkan bahwa Kota Tangerang masih memiliki lahan pertanian yang berpotensi memproduksi padi sawah seluas 1.101 ha, dengan dominasi tanah bertekstur liat dan lempung liat berdebu. Berdasarkan kriteria Pusat Penelitian Tanah (1983), tanah sawah di Kota Tangerang dikategorikan bereaksi masam sampai agak masam (pH 4,7-6,4), berkadar C-organik sangat ) dilakukan di Laboratorium Tanah, Balai Penelitian Tanah. Tingkat kecemaran Cu, Zn, Pb dan Cd tanah dievaluasi berdasarkan nilai indeks contamination/pollution (c/p) menurut prosedur Lacatusu (2000). rendah sampai rendah (0,51-1,98 %) dengan KTK rendah sampai tinggi (8,67-26,62 cmol (+) Konsentrasi total logam berat Cu, Zn, Pb dan Cd dalam tanah di lokasi penelitian masih di bawah batas normal, dengan kisaran Cu 23,9-44,7 mg/kg (terkontaminasi berat sampai sangat berat), Zn 38,0-117,0 mg/kg (terkontaminasi ringan sampai sedang), Pb 12,8-90,6 mg/kg (terkontaminasi ringan sampai sangat berat) dan Cd 0,1-0,3 mg/kg (terkontaminasi ringan sampai sedang). /kg).
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Judul: Sebaran Logam Berat (Cd, Cu, Hg, Pb) pada Sedimen di Perairan Pesisir Kabupaten Tangerang
Abstrak: Pollution of coastal waters caused by Tangerang Regency industrial, domestic, and farmers. Dangerous pollution parameters such as metal cadmium (Cd), copper (Cu), mercury (Hg) and lead (Pb). The concentration of heavy metals in sediment suspended in the water will then settle and accumulate in sediment. This study was conducted in April and August 2013 in the coastal area of Tangerang, Banten. This sampling of water and sediment quality. Method of measurement of heavy metals is ICP and AAS. Analysis of heavy metals is descriptive and statistical. Spearman correlation test is to determine relationship between concentration of heavy metals with grain size particle and total organic carbon. Distribution of heavy metals in sediments varies spatially. The value of the metal content of Hg as a whole has exceeded the quality standard that Hg pollution. The Spearman correlation showed that their is no relationship between heavy metal content with grain size particle (dominated by silt) and total organic carbon showed (P> 0.05).
Keyword: Bogor Agricultural University (IPB), total organic carbon., sediment, grain size particle, Pb element, Hg, Cu, Cd

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