Judul: Optimasi Campuran Ekstrak Air Teh Putih, Hitam, dan Hijau (Camellia sinensis L.) sebagai Antiproliferasi Sel Kanker Payudara MCF-7.
Abstrak: Teh (Camellia sinensis L.) merupakan salah satu tanaman tradisional yang berpotensi sebagai antikanker. Penelitian ini bertujuan menguji aktivitas antiproliferasi ekstrak air teh putih, teh hitam dan teh hijau serta campurannya terhadap sel kanker payudara MCF-7 secara in vitro. Tahapan penelitian yang dilakukan meliputi ekstraksi daun teh, pengukuran kadar air simplisia daun teh, penentuan komponen kimia ekstrak teh, pengujian toksisitas ekstrak teh, serta aktivitas antiproliferasi ekstrak teh terhadap sel kanker payudara MCF-7. Analisis campuran ekstrak teh terhadap aktivitas antiproliferasi sel kanker MCF-7 dilakukan dengan metode simplex-centroid design. Penapisan fitokimia menunjukkan ekstrak teh mengandung senyawa alkaloid, flavonoid, saponin, tanin, dan terpenoid. Toksisitas ekstrak teh hijau, teh hitam dan teh putih ditunjukkan dengan nilai LC50 berturut-turut sebesar 1463.24 μg/mL, 1835.78 μg/mL dan 1243.38 μg/mL. Ekstrak teh putih, teh hitam, teh hijau dan campurannya secara nyata (p˂0.05) mampu menurunkan pertumbuhan sel kanker MCF-7. Hasil analisis menunjukkan model linier yang tidak signifikan terhadap aktivitas penghambatan sel kanker.
Keyword: antiproliferasi, MCF-7, teh hijau, teh hitam, teh putih

Judul: Ekstrak dan Fraksi n-Heksana Teh Hijau sebagai Antiproliferasi Sel Kanker Payudara MCM-B2 In Vitro
Abstrak: Teh hijau (Camellia sinensis) merupakan salah satu tanaman tradisional yang berpotensi sebagai antikanker. Sampel yang digunakan dalam penelitian ini adalah ekstrak teh hijau komersil. Tujuan penelitian ini adalah menguji aktivitas antiproliferasi ekstrak teh hijau terhadap sel kanker payudara MCM-B2 secara in vitro. Ekstrak teh hijau difraksinasi menggunakan tiga pelarut, yaitu air, etanol 70%, dan n-heksana. Ekstrak dan fraksi teh hijau memiliki nilai Lethality Concentration 50 (LC50) lebih dari 1000 ppm. Fraksi etanol 70% dan fraksi nheksana teh hijau memiliki nilai LC50, masing-masing sebesar 883.48 ppm dan 600.56 ppm. Hasil penapisan fitokimia ekstrak teh hijau adalah flavonoid, tanin, dan saponin, sedangkan hasil penapisan fitokimia fraksi n-heksana adalah flavonoid dan tanin. Aktivitas antiproliferasi diuji pada sel kanker payudara MCM-B2 dan sel normal Vero dengan metode pewarnaan tripan biru. Aktivitas penghambatan sel MCM-B2 tertinggi dicapai pada konsentrasi 13000 ppm ekstrak teh hijau dan 1000 ppm fraksi n-heksana, berturut-turut sebesar 59% dan 59%. Ekstrak dan fraksi nheksana teh hijau tidak bersifat toksik terhadap sel normal Vero yang ditandai dengan tidak menghambat proliferasi sel normal.
Keyword: Antiproliferasi, sel kanker MCM-B2, sitotoksisitas, teh hijau komersil

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