Judul: Tingkat Kecemasan, Dukungan Sosial, Dan Mekanisme Koping Terhadap Kelentingan Keluarga Pada Keluarga Dengan Tb Paru Di Kecamatan Ciomas Bogor
Abstrak: General purpose of this research is to know factors that can affect family resilience in families with pulmonary tuberculosis disease at Kecamatan Ciomas, Kabupaten Bogor. The purpose of research are 1) to identify socioeconomic characteristics of families with pulmonary tuberculosis disease, 2) identify the health behavior of pulmonary tuberculosis disease, 3) measuring the level of anxiety patients with pulmonary tuberculosis disease, 4) measure of social support patients with pulmonary tuberculosis disease; 5) measure coping mechanism in families with pulmonary tuberculosis disease; 6) measuring family resilience with pulmonary tuberculosis disease; 7) analyze the correlations between variables of family resilience with pulmonary tuberculosis disease; 8) analyze the influence of variables with family resilience with pulmonary tuberculosis disease. The population of research were family members (parents) as patient with pulmonary tuberculosis disease at Kecamatan Ciomas Bogor, there are: Desa Ciomas, Ciomas Rahayu, and Pagelaran. The subjects in this research are 49 samples chosen purposively. Variables studied were: socioeconomic characteristics, health behavior, anxiety level, social support, coping mechanism, and family resilience. Data analysis using descriptive analysis, correlations to examine relationships between variables, and multiple linear regression to determine the factors that influence the family resilience. The results showed that families with pulmonary tuberculosis disease have good and very good sanitation (73%), good health behavior (57%), anxiety level patient is relatively low to moderate (65%), high coping health mechanism (60%), high coping mechanism (49%), moderate social support (84%), and high family resilience (47%). Based on correlation analysis showed a negative relationship between family income with family resilience. Are positively correlated between family coping health mechanism (CHIP), family coping mechanism, anxiety level, social support with family resilience. Based on multiple linear regression analysis obtained the factors that influence family resilience: large families (β = -0.317, p = 0.003), anxiety level (β = 0.239, p = 0.027), and family coping mechanism (β = 0.511, p = 0.000 .)
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Judul: Strategi Koping, Tekanan Ekonomi, dan Ketahanan Keluarga di Kawasan Kumuh
Abstrak: The aim of this research was to analyze correlations and influences of coping strategy, economy pressure, and family strength. The research was conducted in Sukasari Village, Eastern District of Bogor, Bogor. Samples of this research were family with children aged 3-6 years old that live in urban area. The samples which consist of 90 people were chosen by propotional random sampling. The data collected by interview based on questionnaire. The study showed that there were significant and positive correlation between the age of husband, total income, per capita income, asset ownership and coping strategy with family strength and significant and negative correlation between family size and economy pressure with family strength. By using regression analysis, it show that family strength influenced by economy pressure and coping strategy.
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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