Judul: Perancangan Media Informai Online dan Offline Usaha Ternak Ayam Kampung Berbasiskan Web (studi Kasus di P4S Eka Jaya di Jakarta Selatan)
Abstrak: Sampai saat ini belum ada batasan yang pasti tentang pengertian ayam kampung. Jika di sini disebut-sebut kata “ Ayam Kampung ”, penyebutan ini hanya sekedar untuk menunjukan jenis ayam lokal dengan keragaman genetis tinggi yang sudah dikenal luas dan tersebar di berbagai wilayah Indonesia. Jenis ayam lokal ini diperkirakan menjadi variasi karena pengaruh isolasi tempat. Variasi individu dalam satu jenis ini tidak hanya terbatas pada warna bulu, tetapi juga pada ukuran tubuh, produktivitas telur dan suara. Media informasi online (internet) dan offline (cakram padat) dapat digunakan sebagai salah satu medium komunikasi massa. Saluran komunikasi massa merupakan alat melalui mana sumber komunikasi menyampaikan pesan-pesan kepada penerima. Melalui media ini, diharapkan penerima dapat memperoleh informasi tentang ayam kampung dengan mudah. Disaat tidak dapat menggunakan internet, pengguna dapat memanfaatkan cakram padat sebagai sumber informasi.
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Judul: Analisis Saluran Pemasaran Ayam Kampung (Gallus domesticus) di Jakarta Selatan, Propinsi DKI Jakarta
Abstrak: Ayam kampung merupakan salah satu jenis ternak yang dikenal masyarakat sebagai sumber protein hewani. Usaha peternakan ayam kampung di Jakarta ada 167 buah, yaitu 51 di Jakarta Pusat, 8 di Jakarta Timur dan 108 di Jakarta Barat, dengan populasi ayam sebesar 9.498 ekor. Ketiadaan usaha peternakan ayam kampung di Jakarta Selatan mengakibatkan rendahnya populasi ayam kampung di Jakarta Selatan sehingga permintaan ayam kampung di Jakarta Selatan tidak terpenuhi. Kebutuhan ayam kampung untuk Jakarta Selatan harus dipenuhi dari daerah lain seperti Jawa Barat dan Jawa Tengah. Jauhnya jarak daerah asal ayam kampung dari Jakarta Selatan tentunya mengakibatkan pihak-pihak yang terlibat dalam memasarkan ayam kampung ke Jakarta Selatan akan semakin banyak.
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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