Judul: Tingkat Pencemaran Pesisir Kronjo Kabupaten Tangerang, Banten
Abstrak: Tingkat pencemaran perairan perlu dikaji untuk mengetahui kesesuaian air dengan peruntukannya. Tujuan penelitian ini menentukan tingkat pencemaran di pesisir Kronjo. Metode yang digunakan berdasarkan Indeks Pencemaran (IP) dan Indeks STORET. Hasil penelitian menunjukkan tingkat pencemaran berdasarkan Indeks Pencemaran berkisar antara 2.1–7.0, tergolong tercemar ringan hingga tercemar sedang. Hasil yang berbeda diperoleh jika dievaluasi berdasarkan Indeks STORET. Pencemaran Sungai Cidurian dan Sungai Cipasilian telah tercemar berat dengan Indeks STORET -53 hingga -86. Parameter yang tidak sesuai baku mutu, yaitu kekeruhan, TSS, TDS, BOD5, fenol, CN, NO2-N, DO, O-PO4, H2S, COD, NH3-N, Pb, Cr, Cu, Zn, fecal coliform, dan total coliform. Indeks Pencemaran (IP) menunjukkan untuk laut zona I 1.8-7.1 dan laut zona II 1.2-5.1, kategori tercemar ringan sampai tercemar sedang. Evaluasi dengan STORET menunjukkan laut zona I dan zona II tercemar sedang dengan Indeks STORET - 12 hingga -29. Parameter yang tidak sesuai baku mutu, yaitu kekeruhan, TSS, H2S,O-PO4, dan Pb. Hasil penilaian perbandingan kedua metode tersebut, metode STORET lebih baik digunakan karena memiliki penilaian yang ketat terhadap pencemaran.
Keyword: Indeks Pencemaran, Indeks STORET, pesisir Kronjo, tingkat pencemaran

Judul: Kualitas Perairan Pesisir Cituis Kabupaten Tangerang, Banten.
Abstrak: Masukan limbah dari kegiatan antropogenik di DAS Cirarab wilayah pesisir Cituis Kabupaten Tangerang dapat menurunkan kualitas air Sungai Cirarab dan laut Cituis sebagai tempat bermuaranya sungai ini. Penelitian ini bertujuan untuk menentukan status mutu air Sungai Cirarab dan laut di pesisir Cituis serta menghitung beban pencemaran di laut Cituis akibat masukan bahan pencemar dari aliran Sungai Cirarab berdasarkan karakteristik fisika, kimia dan mikrobiologi air. Penelitian dilakukan di tujuh belas stasiun yang mewakili perairan sungai, muara, dan laut pada bulan April–Agustus 2013. Analisis status mutu air menggunakan Indeks Pencemaran dan Indeks STORET terhadap baku mutu air kelas III dan air laut untuk biota laut. Indeks Pencemaran menunjukkan kondisi perairan tercemar ringan hingga sedang. Indeks STORET menunjukkan Sungai Cirarab dalam kondisi tercemar berat di bagian sungai dan muara, sedangkan Laut Cituis di zona 1 dan 2 kondisinya tercemar sedang pada musim peralihan dan tercemar berat pada musim kemarau. Analisis beban pencemaran menunjukkan adanya beban pencemaran dari parameter kualitas air, seperti TSS, BOD5, fosfat total, NH3, H2S, minyak dan lemak, surfaktan, fenol, Cd, Cr, Cu, dan Pb dengan konsentrasi yang berbeda pada tiap waktu pengamatan.
Keyword: beban pencemaran, Indeks Pencemaran, Indeks STORET

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