Judul: Status kesuburan tanah di daerah reklamasi tailing dan pengaruh keberadaan PT. Freeport Indonesia terhadap pengembangan wilayah di sekitarnya
Abstrak: PT. Freeport Indonesia (PTFI) merupakan perusahaan pertambangan emas dan tembaga yang beroperasi di Kabupaten Mimika, Papua. Dalam kegiatan operasionalnya untuk mendapatkan konsentrat emas dan tembaga, PTFI menghasilkan tailing atau pasir sisa tambang (SIRSAT) yang umumnya mempunyai sifat fisik dan kimia yang tidak baik untuk pertumbuhan vegetasi dan tanaman. Tailing atau pasir sisa tambang yang dihasilkan oleh PTFI diendapkan di kawasan dataran rendah yang disebut Modification Ajkwa Deposition Area (ModADA). Pemulihan area pengendapan tailing ini dapat dilakukan dengan 2 cara, yaitu revegetasi dan suksesi alami. Reklamasi merupakan kegiatan revegetasi yang dilakukan oleh PTFI. Tujuan dari penelitian ini adalah : (1) untuk mempelajari status kesuburan tanah di daerah reklamasi tailing PTFI, (2) untuk mengetahui tingkat hirarki wilayah desa sekitar PTFI, dan (3) untuk mengetahui pengaruh keberadaan PTFI terhadap pengembangan wilayah di sekitarnya.
Keyword: 

Judul: Soil development of mine Tailings in ModADA PTFI : reclamation and natural sucession aspect
Abstrak: Tailings are residue of mining material after separation of valuable substances such as copper, gold and silver elements. Separation of these elements involves crushing of parent material to become fine particles and separation of the precious elements by flotation technique. Total amount of tailings produc ed by PT Freeport Indonesia are about 230.000 tons/day. These tailings are transported and deposited in the lowlands of Timika, Papua and confined in the two levees i.e East Levee and West Levee called as ModADA (Modified Ajkwa Deposition Area). The research had been done in ModADA, the property of PTFI in Timika, Papua from March to November 2005.
Keyword: Tailing ModADA, Reklamasi Tanah, Papua, Succession

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