Judul: Isolasi Karakterisasi Khamir Lokal Penghasil Biosurfaktan
Abstrak: Biosurfaktan adalah senyawa kimia yang dihasilkan oleh mikroba yang memiliki sifat aktif permukaan. Biosurfaktan memiliki beberapa keunggulan dibandingkan surfaktan sintetis, yaitu bersifat biodegmczble, tidak beracun dan struktur kimianya lebih beragam. Biosurfaktan memiliki fungsi yang luas yaitu aktif permukaan, pengemulsi, pembasahan, pelarutan dan penurunan kekentalan. Penggunaan biosurfaktan pada berbagai industri antara lain adalah penambangan minyak bumi, industri kosmetik, malcanan dan minuman, tekstil, kertas, pestisida dan pupuk. Tujuan penelitian ini ialah mendapatkan isolat khamir lokal penghasil biosurfaktan dari tanah tercemar minyak, melakukan karakterisasi isolat dan menguji kemampuan beberapa isolat terpilih untuk memproduksi biosurfaktan dengan menggunakan sumber karbon glukosa dan minyak kelapa sawit atau CPO.
Keyword: 

Judul: Isolasi dan Karakterisasi Bakteri Penghasil Biosurfaktan
Abstrak: Aktivitas petrogenik dan pirogenik di wilayah pesisir DKI Jakarta, khususnya sungai Cilincing mengakibatkan tercemarnya wilayah perairan oleh senyawa hidrokarbon. Bakteri penghasil biosurfaktan dapat dijadikan sebagai alternatif penangan pencemaran tersebut karena bersifat lebih ramah lingkungan dan toksisitasnya rendah jika dibandingkan dengan surfaktan sintetis. Penelitian ini bertujuan untuk mengisolasi dan mengarakterisasi bakteri penghasil biosurfaktan dari sampel tanah Sungai Cilincing. Isolasi bakteri dilakukan dengan menggunakan Mineral Salt Media (MSM) dengan menggunakan sumber karbon berupa solar 2%. Karakterisasi dilakukan dengan menguji aktivitas hemolisis, drop-collapse test, dan oil-spreading test. Sebanyak 19 isolat berhasil diisolasi dari sampel tanah Sungai Cilincing yang dibedakan berdasarkan ciri morfologinya. Berdasarkan hasil pewarnaan Gram, 15 isolat tergolong Gram positif dan 4 isolat tergolong Gram negatif. Dari 19 isolat yang mampu tumbuh pada media MSM +2% solar, sebanyak 9 isolat memberi hasil positif pada lebih dari satu uji dan diduga mampu memproduksi biosurfaktan, namun hanya satu isolat yang memberikan hasil negatif pada pengujian aktivitas hemolisis, yaitu isolat STC234. Isolat tersebut memiliki nilai indeks emulsifikasi sebesar 44,665±0,001%., The petrogenic and pyrogenic activities in the coastal areas of DKI Jakarta, especially the Cilincing River, have resulted in the contamination of water areas by hydrocarbon compounds. Biosurfactant-producing bacteria can be used as an alternative solution to handle the pollution because it is more environmental friendly and has low toxicity compared to synthetic surfactants. This study aims to isolate and characterize biosurfactant-producing bacteria from Cilincing River soil samples. Bacterial isolation was carried out using Mineral Salt Media (MSM) using 2% diesel as a carbon source. Characterization was done by testing the activity of hemolysis, drop-collapse test, and oil-spreading test. Total of 19 isolates were isolated from Cilincing River soil samples which were differentiated based on their morphological characteristics. Based on the results of Gram staining, 15 isolates were classified as Gram positive and 4 isolates classified as Gram negative. From 19 isolates that were able to grow on MSM +2% diesel media, 9 isolates gave positive results in more than one test and assumed to be able to produce biosurfactants, but only one isolate gave negative results on hemolytic activity tests, namely isolate STC234. The isolate had an emulsification index value of 44.665±0.001%.
Keyword: bioremediation, biosurfactant, Cilincing River, emulsification, hydrocarbon

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