Judul: Analisis Pola Makan Dan Aktivitas Fisik Terhadap Usia Menopause Di Kota Bogor
Abstrak: Penelitian ini bertujuan menganalisis pola konsumsi pangan dan aktivitas fisik terhadap usia menopause di Kota Bogor dengan menggunakan disain penelitian cross sectional study. Jumlah sampel dalam penelitian ini 50 orang.Hasil penelitian menunjukan sebagian besar contoh mengalami menopause normal (74%) dan sebanyak 26% mengalami menopause dini.Sebagian besar contoh memiliki status gizi normal yaitu sebanyak 17 orang (34%).Pangan sumber karbohidrat yang paling banyak dikonsumsi contoh dalam sehari adalah nasi dengan frekuensi 2.8 kali dengan berat 560 gram, protein hewani adalah telur ayam dengan frekuensi 0.6 kali dan berat 35.96 gram, pangan kacang-kacangan dan hasil olahannya yaitu tahu dengan frekuensi 0.77 kali dan berat 19.52 gram, dan serat yaitu pepaya degan frekuensi 0.67 kali dan berat 67.21 gram. Tingkat kecukupan energi (72%), protein (62%), dan karbohidrat (62%) contoh sebagian besar termasuk dalam kategori normal.Sementara tingkat kecukupan lemak (36%) defisit berat.Aktivitas contoh sebagian besar termasuk dalam kategori sedang (78%).Tidak terdapat hubungan yang signifikan antara asupan karbohidrat, asupan protein hewani, konsumsi pangan kacang-kacangan dan hasil olahannya (kacang hijau, kacang tanah, tempe), aktivitas fisik, dan faktor reproduksi dengan usia menopause (p>0.05). Terdapat hubungan yang signifikan (p<0.05) antara konsumsi tahu dengan usia menopause.
Keyword: menopause, pola makan, aktivitas fisik, faktor reproduksi

Judul: Hubungan faktor reproduksi, asupan zat gizi dan aktivitas fisik dengan komposisi tubuh wanita pekerja berdasarkan status menopause
Abstrak: The purpose of this study was to examine the association of reproductive factors, nutrient intakes and physical activities with body composition of women worker in Bogor. This study used a cross-sectional design with 69 women workers aged 45-55 years old at Bogor Agricultural University as study participants. Descriptive analysis showed that mean of age at menarche, average of menstrual cycle, and average length of period of study participants were categorized normal. Energy and protein adequacy level were categorized as deficit. Physical activities level were categorized light activity with nutritional status were categorized obese. Almost participants had a high percentage body fat, central obesity, and android body shape. Correlation test showed that age (p=0.065) and the use of hormonal contraception (p=0.054) were positively associated with percentage body fat, meanwhile energy adequacy level (p=0.000), and protein adequacy level (p=0.000) were inversely related to percentage body fat. It might be conclude that age had more influence in increasing body fat, particularly in abdominal area, than menopausal status, nutrition intakes and physical activities in middle-aged women.
Keyword: Bogor Agricultural University (IPB), reproductive factors, physical activity, nutrition intake, body composition, adult women

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