Judul: Kajian Aktivitas Dan Identiflkasi Kelas Senyawa Antibakteri 5 Jenis Madu Indonesia
Abstrak: Madu dikenal seb~gai makanan bcrkhasiat yang ban yak digunakan masyarakat khususnya untuk kcschatan. Madu juga dikcnal scbagai bahan pangan yang memiliki aktivitas antimikroba, antioksidan, antiinflamasi, dan senyawa antibiotika. Pada penelitian ini dilakukan pengujian aktivitas antibakteri dari ekstrak 5 jenis madu alam Indonesia (madu randu, madu rambutan, madu kaliandra, madu mente dan madu kelengkeng) dan identifikasi kelas senyawa yang berpotensi sebagai antibakteri madu. Ekstraksi dilakukan dengan teknik maserasi secara bertahap menggunakan tiga jenis pelarut berdasarkan polaritasnya, yaitu heksana (non polar), etil asetat (semi polar) dan metanol (polar). Ekstraksi dimulai dengan maserasi sampel dalam pelarut heksana menggunakan pengaduk shaker. Maserasi dengan pengadukan ini dilakukan selama 24 jam pada suhu kamar. Setelah 24 jam ekstraksi dilakukan proses pembekuan pada ji-eezer selama 4 jam dengan tujuan untuk memisahkan fraksi yang dalam pelarut heksana yang tidak membeku dan bagian yang membeku. Setelah proses pembekuan selama 4 jam tersebut bag ian yang tidak membeku tersebut dipisahkan dan dihilangkan pelarutnya yang menghasilkan ekstrak heksana.
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Judul: Uji Efektivitas Madu dari Empat Spesies Lebah Berbeda sebagai Agen Antibakteri.
Abstrak: Banyak penyakit yang diakibatkan oleh bakteri. Pengobatan infeksi dapat dilakukan dengan memberikan antibakteri pada luka. Salah satu antibakteri alami adalah madu. Tujuan dari penelitian ini adalah menganalisis efektivitas madu sebagai agen antibakteri. Bakteri target yang digunakan adalah Escherichia coli ATCC 8739, Staphylococcus aureus ATCC 6538 dan Pseudomonas aeruginosa ATCC 15442. Uji antibakteri terdiri dari 4 tahap yaitu uji disk, uji viabilitas, uji pH dan uji proksimat. Hasil uji antimikroba menunjukkan bahwa madu TG1_PND yang berasal dari Stingless Bee memiliki aktivitas antibakteri tertinggi dibanding madu lainnya. Hasil uji viabilitas menunjukkan ketiga bakteri mengalami penghambatan pertumbuhan terbaik dalam konsentrasi madu 100%. Adapun kandungan pH madu diuji melalui pengukuran menggunakan pH meter dan diketahui bahwa madu dengan kode TG1_PND memiliki pH terendah yaitu 2,7. Kandungan air pada madu dapat memengaruhi kualitas madu dan pertumbuhan mikroba., Many diseases are caused by bacteria. Treatment of infection can be done by giving antibacterial to the wound. One natural antibacterial is honey.The purpose of this study was to analyze the potential of honey as an antibacterial. The target bacteria used were Escherichia coli ATCC 8739, Staphylococcus aureus ATCC 6538 and Pseudomonas aeruginosa ATCC 15442. The test consisted of 4 stages, namely disk test, viability test, pH test and proximate test. The antibacterial test results showed that TG1_PND honey from Stingless Bee had the highest antibacterial activity compared to other honeys. The results of the viability test showed that the three bacteria experienced the best growth inhibition in 100% honey concentration. The pH content of honey was tested by measuring using a pH meter and it was found that honey with the code TG1_PND had the lowest pH of 2.7. Water content in honey can affect honey quality and microbial growth.
Keyword: Antibakteri, Bakteri, Madu, Penghambatan, antibacterial, bacteria, honey, inhibition

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