Judul: Potensi Senyawa Antibakteri dari Ekstrak Daun Melastoma candidum Asal Jambi terhadap Escherichia coli Enteropatogen (EPEC)
Abstrak: Tumbuhan Melastoma candidum diketahui banyak dimanfaatkan untuk mengobati berbagai penyakit. Escherichia coli Enteropatogen (EPEC K1.1) merupakan salah satu penyebab utama diare pada balita di Indonesia. Tujuan penelitian ini adalah menguji potensi antibakteri ekstrak kasar dari daun M. candidum asal Jambi terhadap EPEC K1.1 dan mengidentifikasi kandungan dari ekstrak tersebut. Sebanyak 60-70 helai daun M. candidum dimaserasi dengan pelarut metanol. Ekstrak kasar dengan konsentrasi 250 mg/mL, 500 mg/mL, dan 1000 mg/mL diuji kemampuan antibakterinya pada bakteri EPEC K1.1 (108 sel/mL) menggunakan metode difusi agar. Ekstrak kasar dianalisis kandungannya menggunakan GC-MS. Ekstrak kasar daun M. candidum asal Jambi dengan konsentrasi 250 mg/mL, 500 mg/mL, dan 1000 mg/mL dapat menghambat pertumbuhan EPEC K1.1. Senyawa yang dikandung ekstrak tersebut terdiri atas golongan fenol, furan aldehida, dan asam lemak, dengan kadar senyawa aktif paling tinggi yaitu 1,2,3 Benzenatriol.
Keyword: 1,2,3 Benzenatriol, EPEC K1.1, GC-MS, Melastoma candidum

Judul: Pertumbuhan dan Potensi Antibakteri Melastoma malabathricum Akibat Cekaman Kekeringan
Abstrak: Tanaman harendong (Melastoma malabathricum) merespon dan beradaptasi pada cekaman kekeringan dengan metabolit sekunder seperti flavonoid, saponin, dan tanin. Tanaman ini dimanfaatkan oleh masyarakat sebagai obat berbagai penyakit infeksi. Esherichia coli merupakan salah satu penyebab infeksi saluran kemih (Urinary Tract Infections). Penelitian ini bertujuan untuk mempelajari fisiologi dan pertumbuhan tanaman M. malabathricum, serta menguji efek antibakteri dari ekstrak daunnya terhadap E.coli pada kondisi cekaman kekeringan. Daun tanaman M. malabathricum mendapat sembilan perlakuan cekaman kekeringan selama 60 hari dengan dua faktor, yaitu intensitas penyiraman dan volume air sesuai kapasitas lapang. Uji aktivitas antibakteri ekstrak daun M. malabathricum dengan pelarut metanol menggunakan metode Kirby-Bauer dengan modifikasi media. Hasil yang diperoleh dari penelitian ini adalah tanaman M. malabathricum memiliki nilai terendah pada tinggi tanaman, jumlah daun, jumlah anakan, diameter batang, panjang akar, bobot basah akar mati, bobot kering akar mati, bobot kering tajuk, dan klorofil total daun saat tanaman diberi cekaman kekeringan penyiraman setiap 7 hari dan volume air sesuai kapasitas lapang 30%. Uji antibakteri dari ekstrak daun M. malabathricum dengan perlakuan cekaman kekeringan penyiraman setiap 3 hari dan volume air sesuai kapasitas lapang 30% dengan konsentrasi 1 mg/mL dan 5 mg/mL memiliki aktivitas antibakteri tertinggi dan diperoleh diameter daya hambat sebesar 14.50 mm dan 15.67 mm.
Keyword: cekaman kekeringan, infeksi saluran kemih, Melastoma malabathricum

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