Judul: Hubungan antara Konsumsi Pangan dan Aktivitas Fisik dengan Kadar Kolesterol Darah Pria dan Wanita Dewasa di Bogor
Abstrak: The study was conducted to analyze the relationship between food consumption and physical activity levels of blood cholesterol. The specific aims of this study are: 1) to get the data characteristics of respondents, 2) to analyze the relationship between nutrition knowledge with cholesterol intake, 3) to analyze the factors associated with blood cholesterol levels which include intake of fat, carbohydrates, protein, cholesterol, dietary fiber intake, physical activity and gender. The study design was a cross sectional study. Research sites located in the District and the City of Bogor. Respondents in this study were 64 adult men and women aged 25-60 years. Pearson correlation test results showed that there was no significant relationship between nutrition knowledge with cholesterol intake (p>0,05). Stepwise regression test results indicate that the intake of protein, carbohydrate and cholesterol did not significantly affect blood cholesterol levels. At alpha 5%, level of physical activity and gender significantly affect blood cholesterol levels. At alpha 10%, dietary fiber intake, fat intake, physical activity level and gender significantly affect blood cholesterol levels.
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Judul: Hubungan antara konsumsi lemak trans dengan persen lemak tubuh dan status gizi pada orang dewasa di Kabupaten dan Kota Bogor
Abstrak: The general objective of this study was to determine relationship between trans fat intake with percent body fat and nutritional status of adults in Bogor rural and urban area. The research was conducted using a cross sectional study. The number of samples for each region was 48 adults aged 20-65 years which selected purposively. The results showed that most of samples in urban and rural area were in the state of high deficit of energy sufficiency, i.e. 29.17% and 33.33%, respectively. The percentage of fat sufficiency was 43.75% in rural area, which was categorized as sufficient, and 45.83% in urban area, which was categorized as high. Meanwhile, most of the samples (97.92%) in both areas had sufficient level of trans fat intake as recommended. Distribution of body fat percentage in respondents were 24,81 ± 8,44 in rural area and 27,62 ± 8,01 in urban area. Meanwhile, 58,3% of respondents in rural and 64,4% of respondent had normal nutritional status. There was no significant difference between trans fat intake, adequate intakes of energy, total fat, trans fat, and body fat percentage (p>0,05), however there was significant difference between nutritional status of samples in rural and urban area (p<0.05). There was no relationship between adequate intakes of energy, total fat, and trans fat, with body fat percentage and nutritional status (p>0,05), however there was significant correlation between body fat percentages and nutritional status of respondents (p<0,05).
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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