Judul: Reproductive ecobiology of Telmatherina Antoniae (Kottelat, 1991) as the base of the endemic fish conservation In Lake Matano, South Sulawesi
Abstrak: Penelitian kajian ekobiologi ikan opudi Telmatherina antoniae sebagai dasar pengelolaan ikan endemik di Danau Matano, Sulawesi Selatan bertujuan untuk mengkaji karakter habitat, pola distribusi, pertumbuhan dan reproduksi ikan endemik T. antoniae di daerah litoral Danau Matano untuk dijadikan dasar dalam pengelolaan ikan endemik di Danau Matano. Penelitian dilakukan di Danau Matano, Sulawesi Selatan. Sampling dilakukan setiap bulan selama 12 bulan yaitu mulai bulan September 2010 sampai dengan Agustus 2011. Sampling dilakukan pada sembilan stasiun penelitian. Metoda penelitian yang digunakan adalah metoda survei post facto. Stasiun penelitian ditetapkan berdasarkan pertimbangan: (1) merupakan habitat dari ikan T. antoniae, (2) kondisi stasiun penelitian memungkinkan untuk operasional pelaksanaan sampling, dan (3) stasiun penelitian dapat mewakili keragaman habitat dari ikan T. antoniae. Berdasarkan pertimbangan ini ditetapkan sembilan stasiun penelitian yang dibagi dalam tiga zona yang secara spasial mewakili tiga bagian danau utama yaitu: (1) Zona yang mewakili wilayah danau bagian hulu, (2) zona yang mewakili wilayah danau bagian tengah, dan (3) zona yang mewakili wilayah danau bagian hilir.
Keyword: 

Judul: Aspek Pertumbuhan Ikan Opudi (Telmatherina antoniae Kottelat, 1991) di Perairan Danau Matano, Sulawesi Selatan
Abstrak: Perairan Danau Matano mendapatkan masukan limbah dari kegiatan pertambangan PT. Vale Indonesia dan kegiatan masyarakat sehingga dapat mengganggu fungsi habitat ikan opudi (Telmatherina antoniae). Ikan opudi merupakan ikan yang berpotensi sebagai ikan hias karena keunikan yang dimilikinya, yaitu tubuh yang berukuran kecil, sirip yang tidak berkembang, dan warna cerah yang menarik. Penelitian ini bertujuan untuk mengevaluasi aspek pertumbuhan dan habitat ikan opudi (T. antoniae) di perairan Danau Matano, Sulawesi Selatan. Pengambilan sampel dilakukan pada enam stasiun di Danau Matano, Sulawesi Selatan dalam kurun waktu Maret 2018–Februari 2019. Analisis pertumbuhan ikan menggunakan metode Elefan I pada perangkat lunak FiSAT II. Ikan T. antoniae yang ditangkap dengan menggunakan jaring kantong diperoleh sebanyak 4887 ekor. Distribusi frekuensi panjang T. antoniae terendah di daerah Wotu Pali dan pada April 2018, sedangkan tertinggi di daerah Tanah Merah dan pada November 2018. Faktor kondisi T. antoniae jantan berkisar 0,80 – 1,18 dan betina berkisar 0,70 – 1,13. Pola pertumbuhan ikan T. antoniae adalah alometrik negatif dan positif. Pertumbuhan ikan T. antoniae betina lebih cepat dari ikan T. antoniae jantan. Pertumbuhan ikan T. antoniae mencapai nilai optimum di daerah Utuno dan tidak optimum di daerah Wotu Pali. Pertumbuhan ikan T. antoniae mencapai nilai optimum pada bulan November 2018 dan tidak optimum pada bulan April 2018.
Keyword: Growth, Habitat, Matano Lake, Telmatherina antoniae

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