Judul: Uji karakteristik reflektansi sinar ultraviolet dari tiga jenis lampu pada  Heteractis dan Fungia guna pengembangan teknik fluorescence imaging
Abstrak: Penelitian ini bertujuan untuk mengungkapkan karakteristik sinar ultraviolet dari lampu actinic, lampu incandesent dan lampu fluorescence, serta meningkatkan pengetahuan dan kemampuan teknik fluorescence imaging guna penentuan tingkat kesehatan karang. Penelitian dilakukan pada Januari hingga Maret 2007 di Laboratorium Akustik dan Instrumentasi Kelautan, Laboratorium Fisika Lanjut, serta CV. Dinar, Tangerang, Banten.
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Judul: Optimasi Panjang Gelombang Cahaya Lampu Celup Dalam Air sebagai Alat Bantu Penangkap Ikan di Bagan Apung Perairan Barru, Sulawesi Selatan
Abstrak: In this Research, a lamp in water is manufactured as a fishing tool. The lamp is made by super bright light emitting diode (LED) which is assembled on a 9 volt battery power source. There are four kinds of lamp with variated wavelenght, there are blue, yellow, red and white. Experiments of lamp is held in water, which measure the illuminance of light in the air (Iu) and the illuminance of the light in the water (Ia). Through the comparison of Iu and Ia and predicted the illuminance values calculation can be determined within a depth of lamp in water lamp lights on fishing operations. The variation of fishing is the existence of additional lamp in water. The experiment result is the catches with white and blue light are more than yellow and red do. It shows that the wavelenght affect the value of illuminance and the higher illuminance make fishes photothaksis reaction more positive to get more catches.
Keyword: the catches, lights illuminance, LED, lamps in water

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