Judul: Sifat Anti Rayap Zat Ekstraktif Kayu Cempaka Kuning (Michelia Champaca L.)
Abstrak: Kayu Cempaka Kuning (Michella champaca L.) merupakan salah satu jenis kayu yang memiliki kelas awet tinggi, yaitu kelas awet II. Keawetan kayu secara alami ditentukan oleh jenis dan banyaknya zat ekstraktif yang bersifat racun terhadap organisme perusak kayu, yang dlam hal ini adalah rayap. Kayu Cempaka kuning mengandung senyawa alkaloid dan saponin yang diduga bersifat anti rayap. Coptotermes curvignathus Holmgren merupakan rayap tanah yang paling luas serangannya di Indonesia. Tujuan penelitian ini adalah untuk mengetahui kadar zat ekstraktif kayu teras Cempaka Kuning serta pengarusnya terhadap serangan rayap tanah. Penelitian ini dilakukan dalam beberapa tahap yaitu ekstraksi serbuk (40-60 mesh) dengan pelarut aseton dan fraksinasi bertingkat ekstrak aseton dengan n-heksan, etil eter dan etil asetat. Ekstrak aseton yang diperoleh dan fraksi-fraksinya disimpan selama 21 hari terhadap rayap tanah, kemudian dihitung mortalitas serta persentase penurunan berat contoh ujinya.
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Judul: Sifat Anti Rayap Zat Ekstraktif Kayu Secang (Caesalpina sappan Linn.)
Abstrak: Produktivitas hutan sebagai penyedia kayu berkelas awet tinggi dan sudah lama dikenal semakin lama semakin habis, sehingga pengguna akan memanfaatkan kayu fast growing species yang mempunyai keawetan alami rendah. Untuk meningkatkan umur meningkatkan umur pakainya, salah satu cara yang dapat dilakukan adalah melalui pengawetan kayu. Permasalahannya, penggunaan bahan pengawet sintetis yang banyak beredar di pasaran dapat memberikan dampak buruk pada lingkungan karena bersifat nonbiodegradable. Hal ini mendorong menggunakan zat ekstraktif sebagai bahan pengawet kayu yang serdifat renewable dan biodegradable. Penelitian ini bertujuan untuk menentukan kadar zat ekstraktif kayu teras secang serta mengetahui pengaruhnya terhadap serangan rayap tanah.
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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