Judul: Perbandingan Total Mikrob dan Koliform dalam Air Pencucian Karkas Ayam Sebelum Proses dan Saat Proses di Tempat Pemotongan Ayam Kebon Pedes Bogor.
Abstrak: Penelitian ini bertujuan untuk mengidentifikasi total mikrob dan koliform dalam air pencucian karkas ayam sebelum proses dan saat proses di tempat pemotongan ayam kota Bogor. Penelitian ini dilakukan dengan mengambil sampel sebanyak 40 sampel yang terdiri atas 20 sampel air pencucian sebelum proses dan 20 sampel air pencucian saat proses. Pengujian sampel dilakukan dengan metode hitungan cawan untuk pemeriksaan total mikrob dan koliform. Data dianalisis secara deskriptif dan menggunakan metode t-test untuk mengidentifikasi perbedaan. Hasil penelitian menunjukkan bahwa rataan total mikrob dalam air pencucian karkas ayam sebelum dan saat proses secara berurutan yaitu 955 525.0 ± 3 324 094.9 cfu/ml dan 671 038 000.0 ± 1 303 942 389.8 cfu/ml, sedangkan rataan jumlah koliform dalam air pencucian karkas ayam sebelum dan saat proses secara berurutan yaitu 4 302.5 ± 13 051.1 cfu/ml dan 1 048 185.0 ± 1 176 300.4 cfu/ml. Total mikrob dan koliform dalam air pencucian karkas ayam sebelum proses dan saat proses pencucian, menunjukkan perbedaan yang nyata (p<0.05). Tingginya total mikrob dan koliform dalam air pencucian karkas ayam dapat terjadi karena penampungan air pencucian tidak pernah dibersihkan, kontaminasi dari peralatan, tercemar isi jeroan dan feses.
Keyword: air, koliform, total mikrob, pencucian

Judul: Total Mikrob dan Koliform dalam Air dan Limbah Cair Setelah Pengolahan di Rumah Potong Hewan Ruminansia Bubulak Kota Bogor
Abstrak: Penelitian ini bertujuan mengidentifikasi total mikrob dan koliform dalam air dan limbah cair setelah pengolahan di lingkungan Rumah Potong Hewan Ruminansia Bubulak Kota Bogor. Penelitian ini dilakukan dengan mengambil sampel air sumber air, air minum di kandang penampungan, dan limbah cair setelah pengolahan dengan pengulangan sebanyak tiga kali. Pengujian Sampel dilakukan dengan metode hitungan cawan dengan cara tuang untuk pemeriksaan total mikrob dan koliform. Hasil penelitian menunjukkan bahwa rataan total mikrob dalam sumber air, air minum di kandang penampungan, dan limbah cair setelah pengolahan secara berurutan yaitu 17 333.3±3 214.6 cfu/ml, 147 000±32 511.6 cfu/ml, dan 39 366.7±26 071.1 cfu/ml. Rataan total koliform dalam sumber air, air minum di kandang penampungan, dan limbah cair setelah pengolahan secara berurutan yaitu, 187.0±49.6 cfu/ml, 230.0±65.6 cfu/ml, dan 8 333.3±850.5 cfu/ml. Rataan total mikrob dan koliform dalam air dan limbah cair setelah pengolahan di lingkungan Rumah Potong Hewan Ruminansia Bubulak Kota Bogor lebih tinggi dari standar yang dipersyaratkan.
Keyword: air, koliform, limbah cair, rumah potong hewan ruminansia

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