Judul: Mempelajari Aktivitas Antibakteri Bubuk Rimpang Kunyit (Curcuma domestica Val.)
Abstrak: Experiment were conducted to study the antibacterial activity of turmeric powder autoclaved in liquid medium on 6 species of bacteria: Staphylococcus aureus, Streptococcus faecalis, Salmonella gallinarium, Escherichia coli, Bacillus subtilis, and Lactobacillus acidophilus.
Keyword: 

Judul: In Vitro Antibacterial Activity of Turmeric  Extract against Klebsiella sp. Origin of Cloaca Broiler Chicken
Abstrak: Klebsiella sp. adalah bakteri patogen penyebab infeksi saluran pernapasan yang dapat membahayakan manusia serta hewan. Kunyit (Curcuma domestica Val.) adalah salah satu tanaman tradisional yang dimanfaatkan untuk mengatasi resistensi bakteri terhadap antibiotik. Penelitian ini bertujuan untuk mengetahui aktivitas antibakteri dari ekstrak kunyit terhadap isolat Klebsiella sp. secara in vitro. Metode penelitian terdiri dari re-identifikasi isolat Klebsiella sp., proses ekstraksi kunyit dengan metode maserasi menggunakan etanol 96%, lalu proses evaporasi, dan uji fitokimia ekstrak kunyit. Pengujian aktivitas antibakteri ekstrak kunyit dilakukan dengan menggunakan metode difusi sumur dengan konsentrasi 100, 80, 60, 40, dan 20%. Analisis data untuk mengetahui efektivitas berbagai konsentrasi ekstrak kunyit terhadap Klebsiella sp. dilakukan dengan program SPSS. Hasil uji aktivitas antibakteri menunjukkan bahwa ekstrak kunyit memiliki kemampuan untuk menghambat pertumbuhan Klebsiella sp. Hal ini terlihat dengan munculnya diameter zona hambat di sekitar sumur pada konsentrasi 100, 80, 60, 40, dan 20% dengan rata-rata diameter zona hambat secara berurutan 12,2 mm, 11,5 mm, 10,7 mm, 10,7 mm, dan 10,2 mm. Hasil uji Kruskal-Wallis menunjukkan bahwa setiap konsentrasi ekstrak kunyit dapat menghambat pertumbuhan Klebsiella sp. Hasil uji Mann-Whitney menunjukkan hasil yang beragam pada setiap konsentrasinya., Klebsiella sp. is pathogenic bacteria that cause respiratory tract infections that can harm humans and animals. Turmeric (Curcuma domestica Val.) is one of the traditional plants used to treat bacterial resistance to antibiotics. This study aims to determine the in vitro antibacterial activity of turmeric extract against Klebsiella sp. isolates. The research method consisted of re-identification of Klebsiella sp. isolates, the extraction process of turmeric made by maceration method with 96% ethanol, then evaporation process, and phytochemical test of turmeric extract. The antibacterial test done by using well diffusion method with concentrations of 100, 80, 60, 40, and 20%. Data analysis to determine the effectiveness of various concentrations of turmeric extract against Klebsiella sp. was carried out using the SPSS program. The results of the antibacterial activity test showed that turmeric extract had the ability to inhibit the growth of Klebsiella sp. This can be seen by the appearance of the diameter of the inhibition zone around the well at concentrations of 100, 80, 60, 40, and 20% with an average inhibition zone diameter of 12,2 mm, 11,5 mm, 10,7 mm. mm, 10,7 mm, and 10,2 mm. The results of the Kruskal-Wallis test showed that any concentration of turmeric extract could inhibit the growth of Klebsiella sp. The results of the Mann-Whitney test showed varying results at each concentration.
Keyword: antibakteri/antibacterial, bakteri Gram negatif/Gram negative bacteria, difusi/diffusion, kunyit/turmeric, Klebsiella sp

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