Judul: Antiproliferasi sel kanker dan karakterisasi nanokapsul ekstrak mengkudu (morinda citrifolia l)
Abstrak: Badan Kesehatan Dunia (WHO) tahun 2008 menyatakan bahwa sebanyak 7.6 juta orang meninggal akibat kanker. Mengkudu merupakan salah satu tumbuhan herbal yang banyak dijumpai yang bisa mengobati kanker akan tetapi pemanfaatannya belum efisien. Nanokapsulasi dari kitosan merupakan salah satu alternatif peningkatkan efisiensi pemanfaatan ekstrak mengkudu. Ekstrak mengkudu didapat dengan metode maserasi dengan pelarut air. Nanokapsulasi ekstrak mengkudu dibuat dengan metode gelasi ionik menggunakan magnetic stirrer (pengaduk magnetik) dengan kecepatan 1500 rpm lalu hasilnya dikarakterisasi dengan Fourier Transform Infrared (FTIR), Particle Size Analyzer (PSA), dan X-ray Difraction (XRD). Hasil yang diperoleh berdasarkan analisis FTIR adalah terdapat gugus kitosan dan ekstrak mengkudu dalam nanokapsul. Hasil uji PSA menunjukkan ukuran nanokapsul sebesar 192.03 nm. Hasil uji XRD menunjukkan bahwa nanokapsul ekstrak mengkudu memiliki kristalinitas 34.83%. Sitotoksitas (uji potensi hayati) nanokapsul adalah 1.843 ppm. Nanokapsul lalu diuji menggunakan enzim pencernaan, hasil yang didapat menunjukkan bahwa nanokapsul terhidrolisis pada menit ke sembilan. Nanokapsul ekstrak mengkudu diuji menggunakan sel kanker paru-paru (A549), didapatkan hasil konsentrasi 0.5 ppm dapat menghambat proliferasi sel kanker secara signifikan sebesar 4.38%.
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Judul: Pengaruh Nanokapsulasi Terhadap Aktivitas Antioksidan Ekstrak Mengkudu (Morinda citrifolia)
Abstrak: Research and innovation on mengkudu (Morinda citrifolia) extract is needed to increase the efectivity of it’s function as antioxidant. Therefore, mengkudu extract nanocapsule technology was researched for development of mengkudu as antioxidant source. The purpose of this research was to analyzed the influence of mengkudu extract nanocapsulation process to their antioxidant activity with chitosan as the coating agent. Mengkudu extract nanocapsule was made by ionic gelation method. Mengkudu extract nanocapsules were in amorphic phase and the in the size range of 89.15-9774.96 nm for water extraction and 48.99-192.03 nm for ethanol extraction. However, nanocapsulation gave a controlled release effect.
Keyword: antioxidant., nanocapsule, Mengkudu

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