Judul: Analisis Faktor-faktor yang Mempengaruhi Risiko Produksi Benih Ikan Neon Tetra di Kecamatan Bojongsari, Kota Depok.
Abstrak: Petani pembenihan ikan hias neon tetra di Kecamatan Bojongsari menghadapi risiko yang berasal dari penggunaan faktor-faktor produksi. Tujuan penelitian ini adalah menganalisis pengaruh faktor produksi terhadap risiko produksi yang dihadapi petani pembenihan ikan neon tetra di Kecamatan Bojongsari. Penelitian ini mengambil 33 sampel petani di Kecamatan Bojongsari dengan menggunakan purposive sampling sebagai metode penentuan sampel sedangkan metode analisis data menggunakan model Just and Pope. Hasil perhitungan menunjukkan bahwa artemia, kutu air saring, dan tenaga kerja meningkatkan produktivitas secara nyata, sedangkan obat-obatan menurunkan produktivitas secara nyata. Peningkatan penggunaan artemia dan tenaga kerja meningkatkan risiko produksi secara nyata (risk inducing factors), sedangkan indukan dan obat-obatan menurunkan risiko produksi secara nyata (risk reducing factors).
Keyword: Just dan Pope, neon tetra, risiko produksi

Judul: Analisis Risiko Produksi Pembesaran Ikan Hias Neon Tetra (Studi Kasus di Pengusaha Bapak Rodi, Kecamatan Bojongsari, Kota Depok)
Abstrak: Indonesia is the archipelago country and well-known in the world, So it supports the fisheries sector and has the potential for economics development as maritime country and nation. One of these, is the non-consumption of fishery products business in Indonesia, in particulary is a commodity of ornamental fish. It has developed quite rapidly besides having promising prospects economically. The Objective of this study is to identify a source of risk production and to analyze probability and impact of risk production in the rearing operational business of ornamental fish neon tetra owned by Mr. Rodi. The analytical method used is the analysis of risk management and risk analysis based on the size of the Expected Return approach, variance, standard deviation, and coefficient of variation. The result Based on the risk assessment on the production of ornamental fish rearing business neon tetra obtained the expected return value is 84.77 for a single period. That Means is Mr. Rodi can expect as much the result of the acquisition of 84.77 per cent survival rate in this enlargement effort for each harvest period. The coefficient of variation values obtained results of 0.20. In other words, for every one percent success rate ornamental fish neon tetra obtained will have a risk of 0.20 percent at the time of production risk. Some things that can be done is to make investments in the form of a purchase thermometers and pH meters tool that checks the temperature and pH can be done routinely.
Keyword: coefficient variation, standard deviation, variance, Expected Return, Survival rate, Neon Tetra

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