Judul: Potensi Minyak Atsiri Serai Wangi (Cymbopogon nardus) sebagai Antiproliferasi Sel Kanker Payudara MCM-B2.
Abstrak: Salah satu jenis kanker yang menjadi penyebab kematian terbesar ialah kanker payudara. Penelitian terkait pengembangan pengobatan herbal sebagai alternatif antikanker masih perlu dilakukan. Penelitian ini bertujuan menganalisis potensi minyak atsiri dari serai wangi sebagai antiproliferasi sel kanker payudara MCM-B2. Tahapan penelitian ini meliputi penyulingan minyak atsiri serai wangi, penentuan komponen kimia minyak atsiri dengan GC-MS, pengujian toksisitas minyak atsiri, serta aktivitas antiproliferasi minyak atsiri terhadap sel kanker payudara MCM-B2. Hasil penapisan fitokimia minyak atsiri mengandung senyawa alkaloid dan triterpenoid. Senyawa volatil dominan yang terkandung dalam minyak atsiri serai wangi ialah geraniol, sitral, dan mirsen. Uji toksisitas minyak atsiri serai wangi menghasilkan nilai LC50 sebesar 5.34 ppm. Minyak atsiri serai wangi mampu menurunkan pertumbuhan sel kanker MCM-B2 secara nyata (p<0.05). Konsentrasi 3 ppm menunjukkan hasil tidak berbeda nyata terhadap doksorubisin, dan konsentrasi 24 ppm memiliki aktivitas penghambatan tertinggi dengan nilai IC50 sebesar 5.38 ppm.
Keyword: Antiproliferasi, kanker payudara, MCM-B2, minyak atsiri, serai wangi

Judul: Aktivitas Antiproliferasi Ekstrak dan Fraksi Mahkota Dewa (Phaleria macrocarpa) pada Sel Kanker Payudara MCM-B2
Abstrak: Mahkota dewa mengandung flavonoid, dan alkaloid yang berpotensi sebagai antiproliferasi sel kanker. Penelitian ini bertujuan menganalisis antiproliferasi ekstrak dan fraksi mahkota dewa pada sel kanker MCM-B2. Persentase rendemen didapatkan pada ekstrak etanol 10.74%, fraksi n-heksana 3.03%, etil asetat 11.84%, air 54.27%. Nilai LC50 pada ekstrak etanol 13.72 ppm, fraksi n-heksana 147.55 ppm, etil asetat 405.81 ppm, dan air 149.55 ppm. Peningkatan konsentrasi sampel uji berbanding lurus dengan peningkatan antiproliferasi. Konsentrasi terkecil sebagai antiproliferasi MCM-B2 adalah ekstrak etanol 3.5 ppm dan antiproliferasi terbesar adalah ekstrak etanol 56 ppm yaitu 58.28 %. Konsentrasi penghambatan tidak berbeda nyata pada fraksi n-heksana 150 ppm, fraksi etil asetat 1600 ppm, dan fraksi air 75 ppm. Fraksi air memiliki konsentrasi antiproliferase tidak berbeda nyata dengan doksorubisin terkecil dibandingkan fraksi lainnya yaitu 75 ppm dan secara berbeda nyata dengan doksorubisin pada konsentrasi 150 ppm. Nilai IC50 yang teranalisis pada sampel uji adalah sebagai berikut ekstrak etanol sebesar 27.04 ppm, fraksi n-heksana sebesar 1506.48 ppm, fraksi etil asetat 90327.86 ppm, dan fraksi air 514.83 ppm.
Keyword: antiproliferasi, kanker, mahkota Dewa, MCM-B2

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