Judul: Isolasi dan Potensi Antagonistik Bakteri Asosiatif Karang dari Genus Acopora di Perairan Blitar, Jawa Timur
Abstrak: Ekosistem terumbu karang memiliki kerentanan tinggi terhadap perubahan lingkungan yang dapat mengakibatkan terjadinya pemutihan karang. Peristiwa pemutihan karang menyebabkan terjadinya perubahan pada komunitas mikroba (holobiont) karang. Bakteri yang berasosiasi dengan karang memiliki mekanisme pertahanan diri terhadap mikroorganisme yang berpotensi mengganggu inang karang. Potensi antagonistik terbentuk dari adanya aktivitas antagonisme bakteri. Penelitian ini dilakukan terhadap sampel karang Acropora sehat dan yang mengalami pemutihan dari perairan Blitar, Jawa Timur. Isolasi bakteri dilakukan dengan menggunakan metode cawan sebar yang kemudian koloninya dimurnikan dengan metode gores kuadran. Isolat tersebut kemudian ditinjau kemampuan aktivitas antagonisnya menggunakan metode double layer. Didapatkan total 6 sampel karang didapatkan 31 isolat didominasi oleh bentuk morfologi tepian licin, bentuk bundar, elevasi cembung, dan tidak berpigmen (krem) yang merupakan bakteri Gram negatif dengan bentuk coccus. Hasil uji aktivitas antagonis menunjukkan 14 isolat karang sehat yang dapat menghambat 8 isolat karang yang mengalami pemutihan dengan bakteri AcD.14 menjadi bakteri yang paling banyak direspon. Aktivitas hambat yang ditunjukkan hasil uji antagonis sangat bervariasi mulai dari aktivitas lemah hingga aktivitas sangat kuat dengan rata-rata hasil uji memiliki aktivitas kuat dengan luas zona hambat berada pada rentang 11.52±0.12 mm to 19.78±0.13 mm. Sehingga penelitian ini membuktikan adanya potensi bakteri asosiasi karang dalam menghambat potensi patogen karang., Coral reefs ecosystem have high vulnerability to environtmental changes that cause coral bleaching. Coral bleaching events can change coral microbial communities (holobiont). Coral-associated bacteria are known to have self-defense mechanism against microorganisms that have the potential to disrupt coral hosts. Antagonistic potential bacteria is formed when bacteria tested show antagonistic activity. This research was conducted on healthy and bleached corals of Acropora from Blitar waters, East Java. Isolation of coral bacteria was carried out using spread plate method, and later purified using streak plate method. These isolates are evaluated for their antagonistic activity using double layer method. A total of 6 coral samples were obtained, 31 isolates were found which are dominated by morphological forms of smooth edges, round shapes, convex elevations, and not pigmented (beige), which are gram-negative bacteria with coccus shape. The results of the antagonist activity test showed 14 isolates from healthy coral samples could inhibit 8 isolates from bleached coral samples with AcD.14 being the most responsive bacteria. The inhibitory activity shown varied from weak to very strong activity with the average having strong activity with inhibition zone area range from 11.52 ± 0.12 mm to 19.78 ± 0.13 mm. This research proves the potential of coral assosiated bacteria in inhibiting potential coral pathogens.
Keyword: Acropora, aktivitas antagonis, bakteri asosiasi, pemutihan karang

Judul: Penapisan Bakteri Asosiatif Karang Lunak (Octocorallia; Alcyonacea) Sinularia dura dan Lobophytum strictum yang Potensial sebagai Alternatif Penanganan Microfouling di Laut.
Abstrak: Microfouling (pembentukan biofilm) merupakan syarat utama terjadinya biofouling, sehingga untuk melakukan penghambatan terjadinya biofouling pada struktur bangunan laut dapat dilakukan dengan menghambat terjadinya biofilm. Beberapa mikroorganisme laut seperti bakteri yang berasosiasi dengan karang lunak mampu menghasilkan sejumlah metabolit sekunder yang dapat menghambat pertumbuhan biofilm. Tujuan dari penelitian ini adalah mencari bakteri asosiasi potensial dari karang lunak Sinularia dura dan Lobophytum strictum yang dapat menghasilkan senyawa bioaktif sebagai sumber anti-microfouling alami. Penelitian ini dilakukan pada bulan Mei hingga Agustus 2011. Penelitian dibagi menjadi beberapa tahap, yaitu pengambilan sampel karang lunak jenis Sinularia dura dan Lobophytum strictum, penjebakan biofilm bakteri, isolasi bakteri, dan uji antagonisme. Pengambilan sampel karang lunak dan penjebakan biofilm bakteri dilakukan pada waktu dan tempat yang sama yaitu di perairan Barat pulau Pramuka, Kepulauan Seribu. Uji antagonisme dilakukan antara bakteri asosiasi karang lunak terhadap bakteri pembentuk biofilm dengan metode difusi agar. Parameter kualitas perairan (fisika-kimia) yang diukur sebagai data penunjang meliputi suhu, kadar garam, kecerahan, pH, nitrat, fosfat, TSS, dan COD. Hasil penelitian menunjukkan bahwa sebanyak 24 isolat bakteri asosiasi berhasil diisolasi dari karang lunak jenis Sinularia dura dan Lobophytum strictum. Uji antagonisme menunjukkan sebanyak 23 isolat (95,83%) bakteri asosiasi mampu menghambat pertumbuhan bakteri pembentuk biofilm. SD6 dan LS1 merupakan isolat bakteri yang memiliki tingkat aktivitas penghambatan paling tinggi terhadap bakteri pembentuk biofilm. Bakteri SD6 dan LS1 tersebut berpotensi sebagai sumber anti-microfouling alami yang ramah lingkungan.
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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