Judul: Studi potensi makanan ternak itik di Kecamatan Anjatan Kabupaten Indramayu, Jawa Barat
Abstrak: Penelitian ini dilakukan di kecamatan Anjatan, kabupaten Indramayu, Jawa Barat selama satu bulan. Berlangsung dari bulan Maret sampai April 1984. Penelitian ini bertujuan untuk mempelajari sumber daya alam yang dimanfaatkan untuk makanan ternak itik di kecamatan Anjatan. Di samping itu juga mempelajari ketersediaan, pengolahan dan penyajian makanan ternak itik. Untuk ini telah diteliti 45 peternak contoh yang berlokasi di kecamatan Anjatan dengan metode pengambilan contoh secara sederhana.
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Judul: Analisa fungsi produksi dan efisien usaha pada peternakan itik di kecamatan juntinyuat kabupaten indramayu
Abstrak: Penelitian ini dilakukan di kecamatan Juntinyuat, kabupaten Indramayu, Jawa Barat. Waktu penelitian berlangsung dari bulan Maret sampai dengan April 1984. Tujuan dari penelitian ini adalah untuk menentukan fungsi produksi dugaan dari usaha ternak itik rakyat dan menentukan tingkat efisiensinya. Selain itu juga untuk mendapatkan informasi mengenai tingkat penerimaan, pengeluaran dan pendapatan peternak. Untuk itu dilakukan pengambilan contoh sebanyak 45 responden dengan metode acak sederhana. Peubah bebas dalam proses produksi yang dipilih yaitu X Jumlah ternak itik yang dipelihara (ST), X2: Total luas tanah yang dikuasai (Ha), Xz: Total biaya makanan (R), X : Luas kandang yang digunakan (m ), Χ: Jumlah tenaga kerja yang digunakan dalam satu tahun (HRP). Sedangkan peubah tergantung Y, yaitu Nilai penerimaan dari hasil penjualan telur selama setahun (p). Analisa terhadap hubungan peubah bebas dengan peubah tergantung menggunakan analisa fungsi produksi Cobb-Douglas.
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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