Judul: Analisa biaya dan harga pokok makanan ternak di PT. Hirema Industri Makanan Ternak Desa Cijujung Kecamatan Kedunghalang, Bogor (Suatu studi kasus)
Abstrak: Makanan ternak yang dihasilkan oleh industri makanan ternak saat ini merupakan makanan utama ternak unggas khususnya ayam ras. Usaha peternakan ayam ras yang berkembang saat ini adalah usaha pe ternakan kecil, dimana biaya makanan.ternaknya 50- 75 persen dari biaya produksi yang dikeluarkan karena peternak kecil tidak memproduksi sendiri makanan ternak.
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Judul: Analisa biaya produksi dan pendapatan pada perusahaan peternakan ayam ras petelur di Kabupaten Bogor
Abstrak: Penelitian ini bertujuan untuk menganalisa macam dan besarnya biaya produksi dan besarnya pendapatan, serta hubungannya dengan faktor-faktor produksi yang digunakan. Selain itu juga bertujuan untuk menghitung imbangan antara penerimaan dan pengeluaran (BCR), serta rata-rata pengembalian modal (ARR). Data yang digunakan adalah data survey yang dilakukan pada bulan Oktober sampai Nopember 1981 di Kabupaten Bogor. Responden yang diambil adalah sembilan belas perusahaan peternakan ayam ras petelur di Kabupaten Bogor yang dipilih secara acak. Untuk menduga hubungan antara biaya produksi per.. ekor dengan produktivitas digunakan model polynomial pangkat tiga. Untuk mengetahui hubungan antara pendapatan dengan faktor-faktor yang mempengaruhinya, dilakukan pendugaan dengan menggunakan suatu bentuk fungsi pangkat. Hasil yang diperoleh dari penelitian ini menunjukkan bahwa biaya produksi per ekor mempunyai hubungan nyata dengan produktivitas (P/0,05), dengan koefisien determinasi 46,63 persen.
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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