Judul: Keterkaitan Kegiatan Keramba Jaring Apung (KJA) Dengan Tingkat Kesuburan di Waduk Cirata, Jawa Barat
Abstrak: Trophic states monitoring in Cirata Reservoir is an important thing in sustainable management for hydropower plan and floating net cage. The purpose of this research is to analyze trophic states of Cirata Reservoir based on physical, chemical, and biological parameters using some index related to existence of floating net cage in Cirata Reservoir. Trophic states were calculated by some index which is index Nygaard (In), Trophic State Index (TSI), Trophic Index (TRIX), Trophic Level Index (TLI), and Delphi Method (R[DM]). Cluster analysis and principal component analysis indicated that the station group consist of Station KJA that influenced by dissolved oxygen, temperature, Secchi disk depth, and chlorophyll-a, and Station inlet of Citarum River that influenced by total dissolved solid and total phosphate. The results show that the values of In, TSI, TRIX, TLI and R[DM] of Station KJA and inlet of Citarum River are indicated as hypereutrofic. All of the indexs are still relevant to describe the trophic states of Cirata Reservoir.
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Judul: Tingkat pencemaran perairan Waduk Cirata, Jawa Barat: pengaruh sungai dan keramba jaring apung (KJA)
Abstrak: Waduk Cirata merupakan salah satu dari tiga waduk kaskade di Jawa Barat. Letaknya diantara Waduk Saguling (bagian hulu) dan Waduk Ir.H. Djuanda (bagian hilir). Waduk Cirata di bangun pada tahun 1987 dengan luas 6.200 ha. Tujuan utama dibangunnya Waduk Cirata adalah untuk keperluan PLTA, namun pemanfaatan terus berkembang meliputi budidaya ikan dengan sistem Keramba Jaring Apung (KJA), perikanan tangkap, wisata, dan transportasi. Secara internal, Waduk Cirata mendapatkan beban pencemaran dari aktivitas pemanfaatan sedangkan secara eksternal mendapatkan pencemaran dari sungai. Berdasarkan permasalahan tersebut dilakukan penelitian terhadap tingkat pencemaran di beberapa muara sungai dan zona pemanfaatan Waduk Cirata. Tujuan penelitian ini adalah untuk menentukan status mutu air dan tingkat pencemaran perairan Waduk Cirata serta mengidentifikasi sumber pencemaran yang masuk ke Waduk Cirata. Data yang digunakan dalam penelitian ini meliputi data primer dan sekunder. Pengambilan contoh data primer dilakukan pada bulan Februari 2012 dan dianalisis di Laboratorium Pusat Penelitian Sumberdaya Alam dan Lingkungan, Bandung. Data sekunder diperoleh dari hasil pemantauan Badan Pengelola Waduk Cirata (BPWC) setiap tiga bulan dari tahun 2007-2011. Analisis kualitas fisika, kimia, dan biologi perairan dilakukan terhadap 34 parameter. Analisis data kualitas air menggunakan indeks STORET yang dibandingkan dengan baku mutu air menurut Perda Prov. Jawa Barat No.39 Tahun 2000 dan Peraturan Pemerintah Republik Indonesia No.82 Tahun 2001 untuk krgiatan perikanan dan PLTA.
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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