Judul: Peroksidasi Lipid Tikus yang Dinduksi 7,12-dimetilbenz(α)antrasen dengan Pemberian Ekstrak Daun Mindi (Melia azedarach L) dan Temu Putih (Curcuma zedoaria Rosc).
Abstrak: Daun mindi dan rimpang temu putih tergolong tanaman yang memiliki aktivitas antioksidan dan berpotensi sebagai antikanker. Tujuan penelitian ini adalah menguji potensi secara in vivo campuran ekstrak etanol mindi dan temu putih sebagai penentu profil lipid peroksida pada tikus yang diinduksi 12,7-dimetilbenz(a)antrasen (DMBA). Uji sitotoksik campuran ekstrak menggunakan metode BSLT. Profil lipid peroksida diuji dengan metode spektrofotometer untuk mengukur malondialdehid (MDA) pada plasma darah dan jaringan kelenjar mammae tikus. Hasil uji BSLT menunjukkan campuran ekstrak memiliki efek sitotoksik yang kurang kuat. Campuran ekstrak memiliki nilai LC50 sebesar 2676.32 μg/mL. Hasil analsis profil lipid peroksida pada plasma darah tidak menunjukkan pengaruh DMBA dan campuran ekstrak terhadap konsentrasi MDA (p>0.05). Konsentrasi MDA pada jaringan kelenjar mammae tikus tertinggi ditunjukkan oleh kelompok DMBA yaitu sebesar 4.285±0.033 nmol/g. Kelompok campuran ekstrak dosis 500 mg/kg BB cenderung lebih rendah yaitu sebesar 2.669 ± 1.325 nmol/g.
Keyword: lipid peroksida, mindi, sitotoksisitas, temu putih

Judul: Aktivitas Aminotransferase dan Konsentrasi Peroksida Lipid pada Tikus yang Diberi Ekstrak Benalu Teh Campuran (Loranthaceae)
Abstrak: Benalu teh secara tradisional dikenal sebagai antikanker dan biasanya terdiri atas campuran dari dua atau lebih spesies yang termasuk dalam suku Loranthaceae. Tujuan penelitian ini adalah menganalisis khasiat hepatoprotektor dan kandungan fitokimia ekstrak, serta menentukan konsentrasi peroksida lipid serum dengan metode Thiobarbituric Acid Reactive Substances (TBARS) konsentrasi MDA pada 533 nm pada serum darah tikus yang dicekok parasetamol dosis tinggi. Sebanyak 30 ekor tikus jantan (SD) dibagi atas 6 kelompok (n=5): normal, kelompok parasetamol (640 mg/kg BB), kelompok perlakuan + parasetamol (ekstrak 100, 250, 500 mg/kg BB), kontrol positif Curliv-plus + parasetamol (83 mg/kg BB). Penapisan fitokimia pada ekstrak benalu campuran menunjukkan bahwa ekstrak mengandung alkaloid, saponin, flavonoid, fenol, tanin, dan triterpenoid. Perlakuan selama 16 hari pada tikus galur SD menunjukkan bahwa pemberian parasetamol meningkatkan aktivitas ALT sebesar 30.5% dan AST sebesar 46.3%. Perlakuan ekstrak etanol benalu campuran dosis 250 mg/Kg BB mampu menurunkan aktivitas ALT sebesar 28%, aktivitas AST sebesar 18.3%, dan menurunkan konsentrasi MDA pada serum sebesar 53.4% lebih rendah dari kelompok parasetamol. Hal ini menunjukkan bahwa ekstrak benalu campuran mampu melindungi hati dari efek parasetamol.
Keyword: Aminotransferase, Tea mistletoe, Malondialdehyde(MDA), Biokimia, Ekstraksi, Fitokimia, Fisik tikus, Aktivitas ALT, Aktivitas AST

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