Judul: Penentuan Beban Pencemaran dan Status Kualitas Perairan Sungai Karang Mumus Akl'bat Buangan Bahan Organik
Abstrak: Tujuan penelitian ini adalah untuk menentukan beban pencemaran dan status kualitas air Sungai Karang Mumus serta memperkirakan tingkat pencemaran pada waktu yang akan datang. Kegiatan penelitian telah dilaksanakan di perarran Sungai Karang Mumus, Kotamadya Samarinda dan Laboratorium Manajemen Sumberdaya Perairan, Fakultas Pertanian Universitas Mulawarman pada tanggal 5 September -28 Desember 1996. Pengamatan dilakukan di delapan lokas, enam lokasi berada di perairan Sungai Karang Mumus, yaitu stasiun 1 sampai dengan stasiun 6 serta dua lokasi berada di perairan Sungai Mahakam, yaitu stasiun 7 dan stasiun 8. Pengambilan contoh air dilakukan di setiap stasiun secara komposit vertikal dan horizonta dengan menggunakan alat "kemmerer water sampler", sebanyak empat kali dengan interval waktu setiap dua minggu. Stasiun (lokasi) pengambilan contoh untuk hewan bentos makro sama dengan kualitas air. Hewan bentos makro yang berada di alas dan di dalam lumpur sedirnen diambil dengan menggunakan " Ekman grab ". Identifikasi dilakukan dengan mengggunakan kunci identifikasi menurut Quigley (1977), Edmondson (1959) dan Jutting (1953 dan 1956). dst ...
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Judul: Analisis Status Mutu Air dan Formulasi Strategi Pengelolaan Sungai Karang Mumus Kota Samarinda
Abstrak: The aim oj this research was to analyse the water quality status and its effect to ecological component, community structure of makrozoobenthos and g ublic health and also to formulate the strategy jor managing the pollution in l..UJ.arang Mumus river, Samarinda City. This research was conducted from March '$J Mei 2009. Samples of water and makrozoobenthos were taken 3 times every ~onth in 5 stations along the Karang Mumus River. Data of water quality were .ama1yzed using STORET method to find the water quality status. Data of 'ilnakrozoobenthos structure were analyzed to find the abundance, diversity index, ~imilarity index and domination index. Interview also done to find the perseption ~bout pollution from the community along the Karang Mumus River and the ~'Overment. SWOT and AHP were conducted to find the best strategy based on the -:!!lain priority Jor managing the pollution in Karang Mumus River. ; Pollution in Karang Mumlls River caused by community activity along the ~arang Mumus River such as home living, industlY, commercial area that :tfasting the river with the pollutant without any t.hreatment. The result shown that !!he water quality status wasn't suitable Jor the 1st , 2nd and 3rd class standard based ~n PP No. 82 Tahun 2001. It'sjust suitable for 4th class that usefullforfreshwater ~quaculture, ranch and famling activity. The community structure of r:JJ1akrozoobenthos shown the low stability community, waters in stress condition fJrnd some domination of any species. Throught the public health shown that ~'djm'e" was the most disease attack Samm'inda community. This condition showed that the community activity along the Karang Mumus River caused the decrease oj the water quality. Based on SWOT Analysis and AHP, the main priority to manage the pollution are: (1). Coordination with all stakeholder in order to save the environmental of Karang Mumus river, (2). Monitoring, evaluating and cooperating with the community, (3). Socialization the programe to save the Karang Mumus River to all stakeholder, (4). Relocation of the community and normalization of the river (5). Increase self belonging to conserve and len·!! enforcement, (6). Develop the communal water treatment and (7). Develop the ecotourism.
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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