Judul: Elektrode pasta karbon termodifikasi nanopartikel perak untuk deteksi zat aktif pada ekstrak rimpang temu lawak
Abstrak: Temu lawak (Curcuma xanthorriza) adalah tanaman obat yang memiliki banyak khasiat. Tanaman ini mengandung komponen aktif utama berupa kurkuminoid dan minyak atsiri. Pada penelitian ini, dilakukan deteksi komponen aktif dari ekstrak rimpang temu lawak menggunakan elektrode pasta karbon termodifikasi nanopartikel perak. Deteksi tersebut dilakukan dengan cara mengamati pengaruh potensial dan waktu deposisi, pH bufer, serta konsentrasi ekstrak yang dianalisis. Modifikasi nanopartikel perak pada permukaan elektrode dilakukan dengan teknik elektrodeposisi. Voltammogram siklik ekstrak rimpang temu lawak menunjukkan puncak oksidasi pada selang potensial –0.5–1.0 V dengan potensial deposisi optimum –0.4 V selama 800 detik pada pH 6.0. Arus oksidasi yang diamati menunjukkan peningkatan seiring dengan bertambahnya konsentrasi ekstrak.
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Judul: Elektrode Pasta Karbon Termodifikasi Nanopartikel Emas untuk Mendeteksi Zat Aktif pada Ekstrak Temu Lawak
Abstrak: The quality of temu lawak is determined by its active substance that contained therein. Determination of the active substance needs quick and low cost method, as well as, accurate measurement. Voltammetry technique is chosen because it meets these criteria. A gold nanoparticle-modified carbon paste electrode was used as a working electrode and its performances needs to be tested. Performance test was done for several parameters such as potential effects and deposition time, pH, and concentration. The optimum potential and deposition time were 0.5 V and 800 seconds, respectively, while the optimum pH obtained was 6.0. The electrode showed linear response toward concentration of the extract in the range of 0.5˗ 3.5% (w/v) with determination coefficient of 0.9630
Keyword: Bogor Agricultural University (IPB), temu lawak, gold nanoparticle, carbon paste electrode

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