Judul: Aktivitas antioksidan ekstrak daun teh (Camellia sinensis (L.) Kuntze var. assamica) Gambung 3 dari ketinggian tempat tumbuh yang berbeda
Abstrak: Teh merupakan komoditas penting karena komsumsi dan produksi teh dunia meningkat setiap tahun. Kualitas dan aktivitas antioksidan daun teh bergantung kepada senyawa metabolit sekunder yang terkandung di dalamnya. Produksi senyawa ini dipengaruhi oleh faktor lingkungan salah satunya adalah ketinggian tempat tumbuh. Penelitian ini bertujuan mengukur kadar total fenolik, total flavonoid dan aktivitas antioksidan ekstrak daun teh Gambung 3 di tiga ketinggian berbeda, yaitu 800, 1300 dan 1800 mdpl. Kadar total fenolik dan flavonoid diterntukan dengan metode Folin-Ciocalteu dan metode kolorimetrik AlCl3. Penentuan aktivitas antioksidan dilakukan dengan metode peredam radikal bebas DPPH (2,2-difenil-1-pikril hidrazil). Nilai kadar total fenolik tertinggi ditunjukkan pada ketinggian 1300 mdpl sebesar 265.62 GAE mg/g bobot kering. Nilai kadar total flavonoid dan aktivitas antioksidan tertinggi ditunjukkan pada ketinggian 800 mdpl sebesar 109.52 CE mg/g bobot kering dan (IC50) 3.96 ppm. Ketinggian tempat tumbuh hanya memberikan pengaruh yang signifikan terhadap kadar total flavonoid ekstrak daun teh GMB 3.
Keyword: antioxidant, DPPH, phenolics, flavonoids, tea, altitude

Judul: Aktivitas Antioksidan Ekstrak Daun Teh (Camellia sinensis (L.)Kuntze) Dari Ketinggian Tempat Tumbuh yang Berbeda
Abstrak: Teh adalah tanaman yang sensitif terhadap suhu. Suhu yang berbeda pada setiap ketinggian tempat mempengaruhi proses fisiologis teh. Penelitian ini bertujuan menguji total fenolik, flavonoid, dan aktivitas antioksidan daun teh berdasarkan perbedaan ketinggian tempat tumbuh. Total fenolik diuji dengan metode Folin-Ciocalteu pada panjang gelombang 765 nm. Total flavonoid diuji menggunakan metode AlCl3 pada panjang gelombang 510 nm. Aktivitas antioksidan ditentukan dengan metode DPPH pada panjang gelombang 517 nm. Aktivitas antioksidan, total fenolik, dan flavonoid dari ketinggian 690 m dpl lebih kuat daripada ketinggian 1280 dan 1890 m dpl. Hasil analisis menunjukkan daun teh pada ketinggian 690 m dpl memiliki aktivitas antioksidan 5.48 mg L-1 (ppm), total fenolik 290.62 GAE mg g -1 ekstrak, dan total flavonoid 125 CE mg g -1 ekstrak. Hasil ini juga berkorelasi positif dengan aktivitas antioksidan paling tinggi terdapat pada ketinggian 690 m dpl dinyatakan dalam bentuk IC50. Aktivitas antioksidan daun teh dari ketinggian 690 m dpl cukup baik dan dapat dijadikan tolak ukur pemanfaatan daun teh sebagai sumber antioksidan. Kata kunci: antioksidan, DPPH, fenolik, flavonoid, teh
Keyword: Antioxidant, DPPH, Phenolic, Flavonoid, Tea, Biokimia, Rendemen ekstrak daun teh, Konsentrasi total fenolie, Konsentrasi total flavonoid, Aktivitas anti oksigen

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