Judul: Karakterisasi kitinase hasil pemurnian parsial dari isolat bakteri tanah asal Cianjur
Abstrak: Kitinase (EC 3.2.1.14) merupakan enzim yang mampu menghidrolisis kitin yang terdapat dalam eksoskeleton serangga, bakteri, dan jamur, serta enzim ini berpotensi sebagai agen biokontrol. Tujuan penelitian ini adalah memurnikan dan mengkarakterisasi kitinase dari isolat terpilih, serta mengukur kemampuan penghambatan terhadap jamur Colletotrichum sp. oleh bakteri kitinolitik. Aktivitas kitinase ditentukan dengan metode Spindler dan kadar proteinnya ditentukan dengan metode Bradford. Produksi kitinase isolat rizobakteri menunjukkan aktivitas spesifik kitinase tertinggi sebesar 0.0489 U/mg pada isolat C5C dengan pemurnian amonium sulfat 70% serta dapat meningkatkan kemurnian 13.97 kali dibandingkan ekstrak kasar dan fraksi lainnya. Karakterisasi kitinase asal isolat C5C menunjukkan bahwa enzim aktif pada suhu 55 °C, pH 7, waktu inkubasi optimum 120 menit, serta enzim ini memiliki nilai Km sebesar 1.300x 103 mg/L dan Vmaks sebesar 0.0294 mg L -1 detik-1 . Aktivitas antifungi isolat bakteri kitinolitik C5C dapat menghambat pertumbuhan jamur Colletotrichum sp. secara in vitro.
Keyword: Biocontrol, Chitin, Chitinase, Colletotrichum sp., Rhizobacteria, Insect, Bacteria, Fungi, Biocontrol agent, Chitinolitic bacteria, In vitro

Judul: Karakterisasi enzim kitinase asal isolat 11 UJ
Abstrak: Kitinase merupakan enzim yang menghidrolisis polimer kitin menjadi oligomer kitin atau monomer N-asetilglukosamin. Penelitian ini bertujuan melakukan pemurnian parsial dan karakterisasi enzim kitinase untuk memperoleh data ilmiah aktivitas optimum enzim kitinase asal isolat 11 UJ. Tahapan penelitian dimulai dari isolasi enzim kasar kitinase dan purifikasi parsial dengan pengendapan fraksi amonium sulfat 70% dan dialisis. Aktivitas enzim kitinase ditentukan dengan metode Spindler dan kadar protein enzim kitinase ditentukan dengan metode Bradford. Enzim hasil presipitat amonium sulfat 70% dikarakterisasi untuk memperoleh kondisi optimum enzim baik pH, suhu, waktu inkubasi, pengaruh ion logam terhadap aktivitas enzim, serta penentuan nilai Km dan Vmaks. Pemurnian enzim kitinase dengan amonium sulfat 70% dan dialisis menunjukkan faktor pemurnian masing-masing 2.40 kali dengan yield 2.23% dan 5.23 kali dengan yield 0.83% dibandingkan dengan ekstrak kasar enzim. Hasil karakterisasi menunjukkan bahwa enzim kitinase asal isolat 11 UJ mempunyai pH optimum 8, suhu optimum 37°C, dan waktu inkubasi optimum selama 120 menit. Ion Logam Mn, Fe, Cu, Zn2+, dan EDTA dengan konsentrasi 10 mM diketahui dapat berfungsi sebagai inhibitor bagi enzim kitinase asal isolat 11 UJ. Enzim kitinase mempunyai nilai Km sebesar 0.297 µmol/mL dan Vmaks sebesar 4.68 x 105 µmol/mL detik.
Keyword: Chitinase, Isolates 11 UJ, Partial Purification, Characterization

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