Judul: Kajian Metode Pengambilan Sampel Air dalam Rangka Analisis Mutu Mikrobiologi Air Baku Industri di PT MNO Gunung Sindur, Nama NIM Bogor
Abstrak: Water is one of the most important needs in human life. In the food industry, water can be used for various activities such as sanitation, production process or even used as raw material for production. Therefore, water used in production should has its own requirements and standards of quality. PT MNO has its own water supply installation to help fulfilling the needs of raw water for production. Raw water used on any production process need to be analyzed its quality either physical, chemical, or microbiology quality. This study attempts to review the water sampling method used in production, in order to determine the microbiological quality of raw water for production process in PT MNO Gunung Sindur, Bogor. The microbiological analysis performed in this study are the calculation of Total Plate Count, yeast and mold content, as well as examination of coliform bacteria and E. coli. The result of microbiological analysis on water samples after sampling method repairment showed that the average microbiology content in the water samples were reduced compared to the analysis results before the repairment done. The statistical result from both data sets that were proceed with paired samples t-test implied that the reduction occurred on the samples taken with additional treatments has not significant yet. It means the repairment of sampling method had not gave a significant effect but has the potential to reduce the effect of external factors that can affect the result of microbiological analysis
Keyword: Bogor Agricultural University (IPB), yeast, water quality, water, mold, Escherichia coli, coliform

Judul: Studi kualitas air dan limbah cair industri tapioka rakyat di daerah Bogor
Abstrak: Penelitian ini bertujuan untuk mengetahui macam dan kualitas, air yang digunakan pada industri tapioka rakyat di daerah Bogor, serta kualitas limbah yang dibuang ke perairan sekitarnya. Pengamatan dilakukan terhadap cara penjernihan air dan sifat fisika-kimia air seperti kadar oksigen, bahan organik, pH, kekeruhan, besi, sulphat dan lain-lain. Sedangkan sifat limbah yang dianalisa adalah temperatur kekeruhan, pH, karbon dioksida, bahan organik, BOD-5, padatan tersuspensi, kadar oksigen, dan asam sianida. Penilaian kualitas air didasarkan kepada standar air minum menurut Peraturan Menteri Kesehatan RI, Sk. Men. Kes. RI. No. 01/BIRHUKMAS/ 1/75. tgl 26 April 1975. Pemakaian standar tersebut disebabkan belum terciptanya standar air untuk industri tapioka dan karena tapioka digunakan sebagai bahan pangan. Penilaian terhadap limbah didasarkan kepada hasil pengukuran beberapa sifat fisika-kimia serta indikator adanya pertumbuhan organisme terutama ikan.
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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