Judul: Daerah Penangkapan Ikan Ekor Kuning Potensial di Perairan Kepulauan Karimunjawa.
Abstrak: Keberhasilan kegiatan penangkapan ikan ekor kuning salah satunya dipengaruhi oleh musim penangkapan ikan, kondisi ekosistem terumbu karang, dan informasi daerah penangkapan ikan potensial. Tujuan penelitian ini adalah menghitung komposisi jenis dan hasil tangkapan, serta ukuran ikan ekor kuning yang dominan tertangkap, menghitung indeks musim penangkapan ikan ekor kuning, dan memetakan daerah penangkapan ikan ekor kuning potensial di perairan Kepulauan Karimunjawa. Data yang digunakan dalam penelitian ini adalah jenis dan ukuran hasil tangkapan, data hasil tangkapan dan upaya (trip) pada tahun 2013-1017, dan data posisi penangkapan berdasarkan hasil wawancara nelayan. Hasil penelitian menunjukkan bahwa hasil tangkapan ikan ekor kuning yang tertangkap ada dua jenis, yaitu Caesio cuning dan Caesio teres. Alat tangkap yang digunakan adalah bubu dan panah. Hasil tangkapan ikan ekor kuning yang tertangkap dengan bubu lebih banyak Caesio cuning, sedangkan panah didominasi oleh Caesio teres. Ikan ekor kuning yang tertangkap oleh bubu lebih banyak ukuran layak tangkap dibandingkan yang tertangkap oleh panah. Musim puncak penangkapan ikan ekor kuning terjadi pada bulan Februari sampai April. Daerah penangkapan ikan ekor kuning potensial tinggi terdapat di Pulau Parang dan potensial rendah di Pulau Karimunjawa, Pulau Nyamuk, Pulau Kemujan, dan Pulau Genting.
Keyword: daerah penangkapan ikan, ekor kuning (Caesio cuning dan Caesio teres), Kepulauan Karimunjawa

Judul: Zonasi Daerah Penangkapan Ikan Kerapu Ekor Putih (Epinephelus Areolatus) Di Perairan Karimunjawa
Abstrak: Pendugaan daerah penangkapan ikan kerapu ekor putih di perairan Karimunjawa merupakan salah satu faktor keberhasilan nelayan dalam melakukan operasi penangkapan ikan kerapu ekor putih. Tujuan penelitian ini adalah mengevaluasi kriteria-kriteria zonasi potensial penangkapan ikan berdasarkan komposisi ukuran ikan dan CPUE serta menentukan zonasi daerah penangkapan ikan kerapu ekor putih (Epinephelus areolatus) yang potensial di perairan Karimunjawa. Daerah penangkapan ikan diduga dengan menggunakan tiga indikator yaitu CPUE, ukuran ikan yang dominan tertangkap, serta ukuran ikan pertama kali matang gonad. Hasil penelitian menunjukkan nilai CPUE kerapu ekor putih rata-rata adalah 2,11 kg/trip, dan ukuran ikan yang mendominasi adalah jenis kerapu ekor putih dengan ukuran 29-35 cm. Ukuran ikan kerapu ekor putih pertama kali matang gonad yaitu 33 cm. Daerah penangkapan ikan kerapu ekor putih yang potensial terdapat di sekitar Pulau Genting Seruni.
Keyword: CPUE, daerah penangkapan ikan, komposisi ukuran ikan, Perairan Karimunjawa, ukuran ikan pertama kali matang gonad

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