Judul: Sifat Keterawetan dan Keawetan Kayu Durian, Limus, dan Duku terhadap Rayap Kayu Kering, Rayap Tanah, dan Jamur Pelapuk.
Abstrak: Kayu dari hutan rakyat umumnya belum diketahui sifat-sifatnya terutama keawetan dan keterawetannya. Ancaman biodeteorasi di Indonesia sebagai daerah tropis cukup tinggi, sehingga sangat diperlukan tindakan perlindungan berupa pengawetan untuk mencegah hal tersebut. Kayu dari hutan rakyat diharapkan dapat menjadi bahan substitusi kayu dari hutan alam baik untuk kebutuhan komponen bangunan maupun bahan baku industri. Tujuan penelitian ini adalah pertama, untuk mengetahui keawetan kayu durian, limus, dan duku terhadap rayap kayu kering, rayap tanah, jamur pelapuk; kedua untuk mengetahui keterawetan kayu dengan bahan pengawet Diffusol CB dengan metode rendaman dingin dan rendaman panas dingin; dan ketiga untuk mengetahui efektifitas pengawetan dengan Diffusol CB terhadap rayap kayu kering dan rayap tanah.
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Judul: Keterawetan dan Sifat Mekanis Kayu Kecapi, Kayu Rambutan dan Kayu Nangka yang Diawetkan dengan Diffusol CB secara Rendaman Panas Dingin
Abstrak: Berbagai jenis kayu dari hutan yang berasal dari lahan milik rakyat di harapkan, dapat dimanfaatkan sebagaimana kayu komersil untuk konstruksi atau furniture. Namun, untuk itu perlu didukung oleh teknologi yang dapat memperbaiki sifat-sifatnya, salah satunya adalah pengawetan kayu sehingga lebih tahan terhadap biodeteriorisasi yang dapat merusak kayu. Tujuan penelitian ini adalah untuk mengetahui pengaruh perbedaan suhu dan waktu perendaman panas dingin terhadap retensi dan penetrasi bahan pengawet Diffusol CB serta sifat mekanis kayu kecapi (Sandoricum koetjape), kayu rambutan (Nephelium Lappaceum), dan kayu nangka (Arthocarpus heterophyllus).
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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