Judul: Aktivitas Penghambatan Ekstrak Keong Matah Merah (Cerithidea obtusa) Terhadap Artemia salina dan Sel MCF-7.
Abstrak: Ekstrak maserasi aseton keong matah merah mampu menghambat 3 jenis sel kanker yaitu sel kanker Hela/serviks, sel kanker paru/A549, dan sel kanker leukimia/K562. Ekstrak maserasi aseton dan ekstrak air sonikasi keong matah merah bersifat tidak toksik terhadap sel Vero. Tujuan dari penelitian ini untuk menguji aktivitas ekstrak maserasi aseton dan air sonikasi keong matah merah terhadap Artemia salina dan sel kanker payudara (MCF-7) secara in vitro serta mengetahui komponen aktif yang terkandung pada ekstrak. Tahapan dari penelitian ini yaitu karakterisasi bahan (pengamatan morfometrik, preparasi dan analisis proksimat), ekstraksi (air sonikasi dan aseton maserasi) dan karakterisasi ekstrak [aktivitas penghambatan terhadap Artemia salina (BSLT) dan terhadap sel kanker payudara MCF-7 (MTT assay)]. Nilai LC50 terhadap Artemia salina yang dihasilkan oleh ekstrak sonikasi air dan maserasi aseton masing-masing 1082.35 μg/mL dan 749.52 μg/mL. Ekstrak maserasi aseton merupakan ekstrak yang potensial sebagai penghambat sel kanker payudara MCF-7 dimana nilai IC50 yang dihasilkan sebesar 19.36 μg/mL. Komponen aktif yang terkandung dalam ekstrak keong matah merah ialah flavonoid, saponin, dan triterpenoid.
Keyword: Anti kanker, MTT assay, sel kanker payudara

Judul: Aktivitas Antikanker dari Ekstrak Keong Matah merah (Cerithidea obtusa) dengan Metode Maserasi yang Berbeda.
Abstrak: Ekstrak aseton keong matah merah mampu menghambat 3 jenis sel kanker yaitu sel kanker serviks/HeLa, sel kanker paru/A549, dan sel kanker leukimia/K562. Maserasi merupakan metode ekstraksi sederhana yang dilakukan agar komponen aktif pada keong matah merah dapat diambil. Tujuan dari penelitian ini untuk menentukan pengaruh pengadukan dalam ekstraksi komponen aktif keong matah merah dengan metode maserasi terhadap rendemen ekstrak dan aktivitas antikanker. Tahapan dari penelitian ini yaitu preparasi keong matah merah, ekstraksi daging keong matah merah, pengujian toksisitas menggunakan metode Brine Shrimp Lethality Test dan pengujian persen inhibisi terhadap sel kanker HeLa dan MCF-7 menggunakan metode uji Microtetrazolium. Ekstrak aseton keong matah merah yang menggunakan metode pengadukan mengandung senyawa bioaktif yaitu alkaloid, saponin, flavonoid, steroid dan triterpenoid. Rendemen ekstrak tertinggi yaitu pada metode pengadukan sebesar 3.93%. Aktivitas antikanker menunjukkan ekstrak yang menggunakan metode pengadukan memiliki aktivitas tertinggi dalam menghambat pertumbuhan sel kanker HeLa dan MCF-7 dengan nilai penghambatan masing-masing sebesar 72.07% dan 47.53%.
Keyword: anti kanker, sel kanker HeLa, sel kanker MCF-7, uji MTT

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