Judul: Identifikasi Isolat Khamir dengan Internal Transcribed Spacer dan Karakterisasi Morfologinya
Abstrak: Khamir merupakan salah satu keanekaragaman mikroorganisme Indonesia yang melimpah. Penelitian mengenai khamir masih banyak dilakukan dalam bentuk eksplorasi, sedangkan isolasi dan identifikasi keanekaragaman khamir di dunia baru dilakukan 1 % dari 89 genera khamir yang terdaftar pada monograf khamir. Khamir yang baru ditemukan di Indonesia sekitar 42 %. Oleh karena itu, perlu dilakukan isolasi dan identifikasi khamir untuk kepentingan di berbagai bidang. Penelitian ini bertujuan mengetahui identitas molekuler dan morfologi khamir yang diisolasi dari buah kesemek, buah anggur, kayu maros, kayu kalimantan, kayu sumatera, dan ragi komersial. Isolat khamir didentifikasi secara molekuler dengan primer ITS, kemudian ditambahkan dengan karakterisasi morfologi isolat khamir. Isolat khamir hasil identifikasi menunjukkan kemiripan homologi dengan genus Debaryomyces, Wickerhamomyces, Clavispora, dan Metschnikowia. Pohon filogenetik menunjukkan isolat khamir hasil isolasi memiliki kekerabatan yang dekat dengan isolat khamir yang berasal dari data NCBI. Sebagian besar isolat khamir yang diidentifikasi memiliki warna putih susu dan konsistensi yang tidak berlendir.
Keyword: identification, ITS, morphology, yeast

Judul: Identification Yeast from Tempe Production Process based on Internal Transcribe Spacer Ribosomal DNA.
Abstrak: Tempe merupakan salah satu makanan tradisional khas Indonesia yang dibuat dari fermentasi kedelai dengan bantuan Rhizopus, terutama R. microsporus. Pada proses produksi tempe berbagai mikrob seperti bakteri asam laktat serta khamir dapat ditemukan. Adanya khamir pada proses produksi tempe di Indonesia telah dilaporkan sebelumnya, namun jenisnya belum diketahui. Oleh sebab itu, khamir pada proses produksi tempe perlu diidentifikasi. Khamir diidentifikasi dengan pendekatan filogenetik daerah ITS1-5.8S-ITS2 rDNA dan diperkuat dengan karakter morfologinya. Analisis filogenetik dilakukan dengan metode maximum parsimony pada perangkat lunak PAUP* 4.0b. Hasil identifikasi sebelas strain menunjukan bahwa dua strain termasuk strain khamir basidomiset (Kwoniella heveanensis dan Trichosporon asahii), tujuh strain khamir askomiset (Candida rugosa, Exophiala dermatitidis, Issatchenkia orientalis, Kodamaea ohmeri dan Saccharomyces cerevisiae), serta dua strain khamir lainnya diduga berkerabat dengan T. loubieri (basidiomiset) dan Lodderomyces elongisporus (askomiset). Sebanyak tujuh dari sembilan spesies khamir tersebut baru pertama kali dilaporkan keberadaannya pada tempe.
Keyword: Bogor Agricultural University, Institut Pertanian Bogor, IPB, ITS, Phylogenetic, Tempe, Yeast, Food microbiology, Persiapan sumber DNA, Ekstraksi DNA, Isolasi, Amplikasi DNA, Identifikasi molekus, Karakter morfologi

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