Judul: Identifikasi keragaman cendawan yang memengaruhi mutu mikrobiologik bahan baku dan tepung bulu ayam
Abstrak: Tujuan dari penelitian ini adalah mengidentifikasi keanekaragaman cendawan dan pengaruh kualitas mikrobiologik pada produk tepung bulu ayam. Beberapa cendawan mampu diisolasi dari contoh udara pada ruang unloading, pemotongan, pengumpulan dan ruang produksi. Populasi cendawan lebih tinggi pada musim hujan dibandingkan musim kemarau yang didominasi oleh khamir. Kapang lebih banyak diisolasi pada musim kemarau. Cendawan yang diisolasi pada contoh udara meliputi A. flavus, A. niger, Geotrichum, C. albicans, Oidiodendron dan Rhodotorula. C. albicans secara dominan teridentifikasi pada sampel bulu ayam di setiap ruang pengambilan contoh. Khamir ini juga diisolasi dari produk tepung bulu ayam. Pada contoh udara, bahan tambahan dan produk tepung bulu ayam adalah faktor pembeda populasi A. Niger yang diisolasi dari tiga sampel. Ruangan bukan merupakan faktor pembeda terhadap keragaman dan jumlah cendawan yang diisolasi dari contoh udara, bulu dan produksi tepung bulu ayam. Namun, jenis contoh pada ruang produksi merupakan faktor pembeda populasi A. niger yang diisolasi. Kualitas mikrobiologik dari hasil tepung bulu ayam dapat dipengaruhi oleh kualitas udara di ruang produksi. Kualitas mikrobiologik dari ruang pengumpulan dapat mempengaruhi kualitas ruang produksi secara statistik. Ruang pengumpulan dan ruang produksi yang berada pada gedung yang sama merupakan penyebab pengaruh tersebut
Keyword: Bogor Agricultural University, Institut Pertanian Bogor, IPB, Air contamination, Feather meal, Fungi contaminant

Judul: Keanekaragaman Cendawan Yang Diisolasi di Lokasi Perkandangan Ayam
Abstrak: The aim of this research was to identify the kind of fungi in chicken farm environment. Samples which examined in this study were air, feathers and litter taken in two different time periods. Analysis of variance of the population showed diversity of fungi in air and litter sample were came from different population The results showed that fungi that isolated from air sample were A. niger, A. flavus, A. fumigatus, Mucor, and Geotrichum. A. fumigatus population was higher than others in the air sample. A. niger, A. flavus, Candida, and Mucor isolated from feather samples. A. niger populations was higher than other in feather samples. A. niger, A. flavus, A. fumigatus, Candida, Geotrichum, and Mucor isolated from litter samples and A. niger population is higher than others. The level of fungi diversity in Henhouse 1 was higher than Henhouse 2. High population of contaminant fungi in the henhouse would increase the risk of mycoses. The existence of some fungi in chicken feathers, which provide the raw material for producing chicken feathers mealwill affect the quality of the chicken feathersmeal product
Keyword: environment., infection caused by fungi litter, fungi contamination, chicken feathers

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