Judul: Sifat Anti Rayap Zat Ekstraktif Kayu Teras Mindi (Melia azedarach Linn.) terhadap Serangan Rayap Tanah Coptotermes curvignathus Holmgren
Abstrak: Melia azedarach yang dikenal dengan nama mindi merupakan salah satu tanaman cepat tumbuh yang berpotensi sebagai insektisida nabati. Tujuan penelitian ini adalah untuk menentukan kandungan zat ekstraktif kayu teras mindi, menentukan sifat bioaktivitas zat ekstraktif kayu teras mindi terhadap rayap tanah, dan mengidentifikasi senyawa kimia zat ekstraktif kayu teras mindi. Perlakuan pada penelitian ini terdiri dari perlakuan kontrol, dan pemberian ekstrak fraksi n-heksan, etil eter, etil asetat, dan residu dengan konsentrasi 2, 4, 6, 8, dan 10%, serta identifikasi senyawa kimia zat ekstraktif menggunakan pyr-GCMS. Hasil penelitian menunjukkan bahwa kandungan zat esktraktif kayu teras mindi yang diperoleh sebesar 0.74%, dan menunjukkan adanya sifat bioaktivitas anti rayap tertinggi pada fraksi n-heksan konsentrasi 8% dan 10% dengan mortalitas rayap tertinggi yaitu 100%, dan kehilangan berat kertas terendah sebesar 13.50% dan 12.29%. Senyawa kimia dominan yang terkandung dalam zat ekstraktif kayu teras mindi yaitu asam palmitat, olealdehida, stearil aldehida, dan androstenediol.
Keyword: kayu teras, mindi (Melia azedarach Linn.), rayap tanah Coptotermes curvignathus Holmgren, zat ekstraktif

Judul: Sifat Anti Rayap Zat Ekstraktif Kulit Kayu Mindi (Melia azedarach Linn.) terhadap Rayap Tanah Coptotermes curvignathus Holmgren
Abstrak: Melia azedarach Linn. yang dikenal dengan nama mindi adalah pohon cepat tumbuh yang banyak ditanam saat ini untuk kebutuhan bahan baku industri kayu. Tujuan penelitian ini adalah memperoleh kadar zat ekstraktif kulit kayu mindi dan mengetahui sifat bioaktivitasnya terhadap rayap tanah Coptotermes curvignathus Holmgren serta mengidentifikasi kandungan senyawa bioaktifnya. Hasil penelitian menunjukkan bahwa kandungan zat ekstraktif kulit mindi sebesar 7.54%. Ekstrak asetonnya difraksinasi bertingkat dengan pelarut n-heksan, etil eter, dan etil asetat. Uji sifat anti rayap menunjukkan bahwa semua fraksi (n-heksan, etil eter, etil asetat, dan residu) memiliki sifat anti rayap dengan fraksi teraktifnya adalah fraksi etil asetat. Persentase mortalitas fraksi etil asetat ini mencapai 100% pada konsentrasi 6%, 8%, dan 10% serta kehilangan berat kertas uji terendah sebesar 6.3% pada konsentrasi 10%. Berdasarkan analisis Pyrolysis Gas Chromatography Mass Spectrometry (Pyr-GCMS) diketahui bahwa ada 6 komponen utama yang terdapat dalam fraksi etil asetat, yaitu pirokatekol, asam asetat, katekol, dioktil ftalat, asam palmitat, sitosterol, dan kelompok asam lainnya seperti asam karbonat, asam sinamat, dan asam miristat.
Keyword: Coptotermes curvignathus Holmgren, ekstraktif, Melia azedarach Linn., sifat anti rayap

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