Judul: Kondisi Sanitasi Tangan Food Handler pada Warung Makanan di Sekitar Kampus Institut Pertanian Bogor Dramaga Bogor
Abstrak: The aim of this study was to assess of hand sanitation of food handlers and identify risk factors on hygiene practices. Total of 42 food kiosks around the Bogor Agricultural University campus in Dramaga Bogor were selected randomly as unit samples and one of food handlers in each kiosks was selected as a respondent. The research was conducted with using questionnaires and counting of microorganism number with RODAC method. The result showed that most of food handlers were in the age range of adults and old (73.8%). More than half of respondents had attended school until senior high school (57.1%), new experience of trading (69.1%), no attended trainings (71.4%), and no subjected to inspections (71.4%). Generally the food handlers had good knowledge, moderate attitude, and good practices. There was a significant correlation (r=0.752; p<0.05) between knowledge and practices. Only few of food handlers (16.6%) were categorized in acceptable criteria of total plate counts of hand surfaces. The identified risk factors on hygiene level were young age (OR=9.80), adult age (OR=8.40), and low level education (OR=12.14). This condition could be a potential risk for consumer health.
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Judul: Kondisi sanitasi tangan food handler pada warung makanan di dalam Kampus Institut Pertanian Bogor (IPB) Dramaga Bogor
Abstrak: This study was aimed to know condition of hands sanitation of food handler and to identify risk factors. Total of 35 food kiosks was involved in this study which was selected randomly. One of food handlers in each kiosks was randomly selected to be a respondent. The data were collected using questionnaires and counting of microorganism number using RODAC method. The result showed that most of food handlers were in the age of young and adults (82.9%), more than half of respondents had attended school until senior high school (65.7%), new trade experience (51.4%), attended trainings (57.1%), and been subjected to inspections (57.1%). Generally, the food handlers had good knowledge, moderate attitude, and good practices. There was a significant correlation (p<0.05) among knowledge, attitude, and practices, and the correlation between knowledge and practices was the highest (r=0.752). Only few of food handlers (14.3%) were categorized in acceptable criteria of total plate counts of hand surfaces. It indicated that the personal hygiene practices in the kiosks was still low. Since the food kiosks inside of campus are very important for the consumers health, the campus authority should take into account of improvement of food hygiene practices in the food kiosks.
Keyword: attitude, food handler, food kioks, knowledge, practices.

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