Judul: Profil Metabolit Sekunder Rhizopus microsporus Pembawa Endobakteri.
Abstrak: Curtobacterium sp. dilaporkan terdapat di dalam hifa R. microsporus IPBCC 13.1131 dari tempe segar asal Cilacap. Adanya endobakteri dapat mempengaruhi perilaku cendawan inangnya dan pengaruh endobakteri ini terhadap inangnya belum diketahui. Penelitian ini bertujuan menganalisis profil metabolit sekunder dan deteksi endobakteri R. microsporus IPBCC 13.1127 dari tempe asal Medan. Dua strain R. microsporus, IPBCC 13.1131 dan 13.1137 asal tempe digunakan sebagai pembanding. Metabolit diproduksi pada media cair Glycerol Yeast yang diagitasi pada kecepatan 120 rpm selama 11 hari. Ekstrak etil asetat metabolit ekstraselular R. microsporus dianalisis menggunakan High Performance Liquid Chromatography pada 220 nm. Adanya endobakteri dideteksi dengan pewarnaan LIVE/DEAD® Bacterial Viability Kit dan dikonfirmasi secara molekuler dengan metode PCR menggunakan set primer 10F/1541R. Hasil penelitian menunjukkan profil metabolit sekunder R. microsporus IPBCC 13.1127 lebih mirip dengan profil strain IPBCC 13.1131 (99%) daripada profil strain IPBCC 13.1137 (75%). Deteksi endobakteri menunjukkan bahwa R. microsporus IPBCC 13.1127 mengandung endobakteri. Informasi ini dapat dijadikan pertimbangan perlunya deteksi endobakteri pada starter tempe.
Keyword: Bakteri endohifa, Curtobacterium, HPLC, molekuler, starter tempe

Judul: Deteksi dan Identifikasi Endobakteri dalam Hifa Rhizopus spp.
Abstrak: Banyak spesies Rhizopus (Rhizopodaceae, Mucorales) digunakan sebagai agens bio-industri. Laporan adanya endobakteri pada R. microsporus yang memproduksi toksin mendorong munculnya gagasan bahwa Rhizopus spp. agens bio-industri harus bersifat aman. Keamanan Rhizopus inokulan tempe berdasarkan keberadaan endobakteri belum diketahui. Oleh sebab itu, keberadaan endobakteri pada 7 strain Rhizopus spp. asal tempe, dan 4 strain Rhizopus spp. asal substrat lainnya diteliti. Satu strain Rhizopodopsis javensis dan satu strain Escherichia coli digunakan berturut-turut sebagai kontrol negatif dan kontrol positif. Keberadaan endobakteri dideteksi dengan dua cara, yaitu secara molekuler dan pewarnaan. Deteksi secara molekuler menggunakan metode PCR 16S rDNA dengan primer 10F dan 1541R. Konfirmasi dilakukan dengan pewarnaan LIVE/DEAD® Bacterial Viability Kit. Bakteri diidentifikasi secara molekuler melalui pendekatan filogenetik, berdasarkan metode Neighbour-Joining dan model p-distance pada MEGA 6 dengan endobakteri pada Mortierella elongata sebagai outgroup. Hasil penelitian menunjukkan satu kultur, R. microsporus IPBCC 13.1131 asal tempe dari Cilacap, Jawa Tengah, mengandung endobakteri. Adanya endobakteri terkonfirmasi dengan munculnya pendaran merah dan hijau dalam hifa, namun tidak ada endobakteri yang terdeteksi pada spora. Endobakteri ini diidentifikasi sebagai Curtobacterium sp. Adanya endobakteri Curtobacterium pada R. microsporus pertama kali dilaporkan. Metode deteksi ini dapat diusulkan menjadi salah satu prosedur jaminan mutu kualitas Rhizopus spp. agens bio-industri.
Keyword: Curtobacterium, endobakteri, pewarnaan, Mucorales, 16S rDNA

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