Judul: Toksisitas dan Hepatoproteksi Akibat Pemberian Campuran Fraksi Flavonoid Jati Belanda-Jabon Merah pada Tikus Stres Oksidatif
Abstrak: Jati belanda dan jabon merah tergolong tanaman hutan yang memiliki aktivitas antioksidan dan berpotensi memiliki aktivitas hepatoproteksi. Toksisitas campuran fraksi flvonoid kedua tanaman tersebut juga belum diketahui. Penelitian ini bertujuan menentukan toksisitas formula fraksi flavonoid daun jati belanda dan jabon merah serta aktivitas hepatoproteksinya pada tikus stres oksidatif. Uji toksisitas akut terhadap fraksi flavonoid daun jati belanda dan campurannya dengan jabon merah menggunakan panduan OECD 425. Percobaan aktivitas hepatoproteksi formula A (jabon merah), formula D (campuran fraksi jati belanda dan jabon merah) serta formula G (jati belanda) dilakukan pada tikus stres oksidatif yang diinduksi oleh parasetamol. Formula D dan formula G memiliki nilai LD50 lebih besar dari 5 g/kg BB. Semua formula fraksi flavonoid dapat menghambat peningkatan aktivitas aminotransferase pada serum darah tikus dan berpotensi sebagai hepatoprotektor. Formula D menunjukkan aktivitas hepatoproteksi tertinggi dengan presentase penurunan enzim ALT dan AST sebesar 34.56% dan 39.32%. Aktivitas hepatoproteksi formula D sejalan dengan aktivitas antioksidannya yang ditunjukkan dengan konsentrasi MDA serum sebesar 47.95% lebih rendah dibandingkan kelompok kontrol parasetamol.
Keyword: A. macrophylus, antioxidant, flavonoid, G. ulmifolia, hepatoprotective

Judul: Toksisitas Akut dan Aktivitas Penghambatan Peroksidasi Lipid oleh Fraksi Flavonoid Daun Jabon Merah In Vivo
Abstrak: Daun jabon merah (Anthocephalus macrophyllus) merupakan salah satu tanaman obat yang memiliki potensi sebagai antioksidan. Penelitian ini bertujuan menguji keamanan dan penghambatan peroksidasi lipid oleh fraksi flavonoid daun jabon merah. Fraksi diperoleh dengan cara maserasi menggunakan etanol 50% yang dilanjutkan ekstraksi cair-cair dengan pelarut n-heksana dan kloroform. Keamanan fraksi diuji dengan metode OECD 425 pada dosis 2000 mg/kg BB dan 5000 mg/kg BB. Aktivitas antioksidan diuji pada tikus Sprague dawley yang dibagi menjadi 6 kelompok (n=5). Kelompok normal dicekok akuades dan 5 kelompok lainnya dicekok parasetamol (640 mg/kg BB), curliv plus® (200 mg/kg BB), dan fraksi (100 mg/kg BB, 200 mg/kg BB, dan 400 mg/kg BB) selama 16 hari. Hasil penelitian menunjukkan bahwa fraksi flavonoid tergolong praktis tidak toksik (LD50 > 5000 mg/kg BB). Pemberian fraksi flavonoid dosis 100 mg/kg BB, 200 mg/kg BB, dan 400 mg/kg BB sampai hari ke-16 dapat menghambat lipid peroksida berturut-turut sebesar 52.06%, 33.10%, dan 29.70%. Data ini menunjukkan bahwa pemberian fraksi flavonoid daun jabon merah berpotensi sebagai antioksidan in vivo.
Keyword: Antioksidan, Flavonoid, Jabon merah, LD50, Lipid peroksidat

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