Judul: Hubungan Karakteristik Peneliti Peternakan Badan Penelitian dan Pengembangan Pertanian dengan Frekuensi dan Intensitas Membaca "Jurnal Penelitian dan Pengembangan Pertanian"
Abstrak: Penelitian ini dilakukan diBalai Penelitian Ternak Ciawi - Jawa Barat, Sub Balai Penelitian Ternak Klepu - Jawa Terigah dan Sub Balai Penelitian Ternak Gowa - Sulawesi Selatan , denyan jumlah responden sebanyak 62 peneliti . Data yang dikumpulkan dari responden berupa karakteristik demografik dan psikografik responden, artikel yang dibaca, frekuensi membaca, intensitas membaca serta evaluasi tentang "Jurnal Penelitian dan Pengembangan Pertanian M ( Jurnal) .
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Judul: Hubungan Beberapa Larakteristik Pembaca "Poultry Indonesia" Di Kabupaten Dan Kotamadya Bogor Dan Frekuensi Mereka Membaca Majalah Tersebut
Abstrak: Penelitian ini dilakukan di Kabupaten dan Kotamadya Bogor dengan tujuan untuk (1) menentukan karakteristik demografik dan karakteristik psikografik pembaca majalah "Poultry Indonesia," (2) menentukan artikel-artikel yang dibaca oleh pembaca majalah "Poultry Indonesia," (3) menentukan motivasi mereka membaca majalah "Poultry Indonesia," (4) menentukan hubungan motivasi membaca pembaca dan frekuensi mereka membaca artikel-artikel "Poultry Indonesia," dan (5) menentukan hubungan beberapa karakteristik pembaca dan frekuensi mereka membaca artikel-artikel majalah "Poultry Indonesia." 35 responden pembaca majalah "Poultry Indonesia" yang dilibatkan dalam penelitian ini dipilih secara acak berlapis proporsional dari daftar populasi pembaca tetap majalah tersebut. Desain yang digunakan dalam penelitian ini ialah suatu survai deskriptif korelasional dengan variabel bebas motivasi membaca "Poultry Indonesia" dan beberapa karakteristik pembaca majalah tersebut dan variabel tak bebas frekuensi membaca artikel-artikel majalah "Poultry Indonesia." Data karakteristik pembaca yang diukur ialah: (1) jenis kelamin, (2) umur, (3) tingkat pendidikan terakhir, (4) pekerjaan pokok, (5) lokasi tempat tinggal, (6) jarak tempat tinggal ke kota, (7) skala pemilikan ayam ras, (8) frekuensi mengunjungi tempat rekreasi, (9) frekuensi menonton film, (10) frekuensi berbelanja kebutuhan sandang, (11) frekuensi mengunjungi rumah makan, (12) frekuensi bepergian ke luar kota, (13) kebiasaan berbicara dalam suatu forum, (14) keterbukaan terhadap inovasi peternakan, dan (15) keikutsertaan dalam suatu organisasi. Data lain yang dikumpulkan ialah artikel-artikel majalah "Poultry Indonesia" yang dibaca, intensitas membaca artikel tersebut dan derajat kesukaan terhadap artikel-artikel tertentu. Data tentang motivasi diidentifikasi oleh indikator motivasi yaitu: (1) menambah pengetahuan, (2) menambah ketrampilan beternak, (3) mencari berita, (4) mencari informasi pasar, (5) mengetahui inovasi peternakan, (6) mengisi waktu senggang, (7) kebiasaan atau hobi, dan (8) mencari bahan pembicaraan. ...
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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