Judul: Studi Kasus: Kelompok Usaha Pengrajin Tahu Tempe di Kedaung, Ciputat- Banten
Abstrak: Empowerment is an activity to make a powerless individual or group becomes powerful. Empowerment is usually done by government, private, or NGOs toward civil society, whom can not able to make themselves independent. The process of empowerment is generally done by giving information and knowledge, skills, as well as chances to implement those skills. However, that case was not always occurring on the real life. The life of “Tahu-Tempe”craftsman makers in Kedaung was one of the examples. They used their own social capital as an apparatus to make their relatives powerful. This showed that civil society was also able to execute the empowerment process to increase their independent and prosperity.
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Judul: (Studi Kasus: Kelompok Usaha Kecil Jins di Cipulir,  Jakarta Selatan )
Abstrak: This research purpose was to analyze the empowerment strategy in Small and Medium Enterprise (SMEP) program and to analyze the empowerment process in SMEP program. This program has operated by Swisscontact (one of the international NGO), and the beneficiaries is a denim cluster in Cipulir. This method research was qualitative method and sampling method was snowball sampling. The result was empowering strategy that Swisscontact use is facilitation strategy. It means the denim cluster in Cipulir is already knows what they problem but they don’t know how to solve it. This strategy make Swisscontact has an agent of change in this SMEP program. The second result was, empowering process in this program split in two phases. First is primer process, in this process the denim cluster in Cipulir having a training to improve their productivity. Second is secondary process, in this process the denim cluster in Cipulir having a group discussion with Swisscontact and other players in this area. The discussion purpose is to encourage and motivated them during this program.
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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