Judul: Konsumsi serat dan fast food serta aktivitas fisik orang dewasa yang berstatus gizi obes dan normal
Abstrak: Tujuan umum dari penelitian ini adalah mempelajari konsumsi serat dan fastfood serta aktivitas fisik orang dewasa yang berstatus gizi obes dan normal. Tujuan khusus dari penelitian ini adalah 1) mempelajari karakteristik individu dewasa berstatus gizi obes dan normal, 2) mempelajari konsumsi serat individu dewasa berstatus gizi obes dan normal, 3) mempelajari konsumsi fastfood individu dewasa berstatus gizi obes dan normal, 4) mempelajari aktivitas fisik individu dewasa berstatus gizi obes dan normal, 5) menganalisis hubungan karakteristik individu, konsumsi serat, konsumsi fastfood dan aktivitas fisik. Desain penelitian ini adalah cross sectional study. Lokasi penelitian dilakukan di Kampus IPB (Institut Pertanian Bogor) pada bulan Oktober- November 2010. Penelitian ini merupakan bagian penelitian yang berjudul ”Efikasi Temulawak (Curcuma Xanthorriza Roxb.) dengan bahan aktif Xanthorrhizol untuk meningkatkan Imunitas pada Orang Dewasa Obes”. Contoh dalam penelitian ini adalah pegawai di Kampus IPB. Pemilihan contoh dilakukan berdasarkan kriteria contoh yaitu pegawai di kampus IPB yang memiliki status gizi normal dan obes. Metode pengambilan contoh yaitu dengan cara purposif dengan kriteria jenis kelamin pria dan wanita yang berumur lebih dari 21 tahun dan bersedia menjadi contoh dalam penelitian ini. Jumlah dibagi dalam 2 kelompok, yang berstatus gizi obese dan normal, dengan jumlah per kelompok 25 orang.
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Judul: Gaya Hidup, Intake Zat Gizi Dan Morbiditas Orang Dewasa Yang Berstatus Gizi Obes Dan Normal
Abstrak: The objective of this research was to identify characteristic of samples, food intake, lifestyle, morbidity and energy balance among adult obese and normal nutritional status. A cross-sectional study was conducted from October to November 2010 in Bogor Agricultural University (BAU). The study participants were employee’s rector of BAU. The samples were chosen purposively with criteria were adult category, age >21 years, male and female gender, body mass index (BMI) >27.0 for obese nutritional status; BMI 18.5-25.0 for normal nutritional status, and ready for being sample of research. The number of obese sample were 25 and normal sample 25 people. Majority (72%) of obese samples were 30-49 years old. There was a significant difference in education levels of among samples (p<0.05). Rice, cassava, street food and soft drink consumption were found higher in obese than normal samples. Statistical analysis showed that there was a significant difference in obese and normal samples in protein intakes and adequacy level of protein (p<0.05). There was a significant difference in leisure-time between obese and normal samples (p<0.05). Ready-to-eat food habit was found higher in obese than normal status. The average of energy balance among samples were negative. In addition, there was a significant difference in family history of obesity between obese and normal samples (p<0.05). For the last three month, both subjects suffer from infections of acute respiratory. Diabetes mellitus and uric acid was found in obese subjects, while anemia was found in normal subjects The study showed that nutritional status was positively correlated with age, education levels, frequency of cassava consumption, family history of obesity and ready-to-eat food habit and negatively associated with intakes of protein.
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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