Judul: Faktor Risiko Hipertensi pada Ibu Rumah Tangga di Desa Pemali Kabupaten Bangka
Abstrak: Penelitian ini bertujuan untuk menganalisis faktor risiko hipertensi pada ibu rumah tangga di Desa Pemali Kabupaten Bangka. Desain penelitian yang digunakan adalah cross sectional melibatkan 97 orang yang dipilih secara consecutive dengan kriteria inklusi yaitu ibu rumah tangga berusia 31-55 tahun, tidak hamil dan menyusui, serta bersedia menjadi subjek penelitian. Hasil penelitian menunjukan prevalensi hipertensi pada ibu rumah tangga di Desa Pemali sebesar 32.9%. Berdasarkan hasil uji korelasi Spearman, terdapat hubungan signifikan antara usia (p = 0.005, r = 0.282), status menopause (p = 0.007, r = 0.274), pendapatan (p = 0.022, r = -0.232), indeks masa tubuh (IMT) (p = 0.023, r = 0.231), lingkar perut (p = 0.008, r = 0.267), dan riwayat keturunan hipertensi (p = 0.003, r = 0.302) dengan kejadian hipertensi. Hasil uji regresi logistik menunjukan peningkatan usia diatas 45 tahun (OR= 5.99, 95% CI 1.82-19.65), IMT diatas 25 kg/m2 (OR=3.26, 95% CI 1.5-9.25), dan adanya riwayat keturunan hipertensi (OR= 2.71, 95% CI 0.86-6.26) secara signifikan akan meningkatkan kejadian hipertensi.
Keyword: faktor risiko, hipertensi, ibu rumah tangga

Judul: Faktor Risiko Kejadian Obesitas pada Ibu Rumah Tangga di Desa Lingkar Kampus Institut Pertanian Bogor.
Abstrak: Penelitian ini bertujuan mengetahui faktor risiko kejadian obesitas pada ibu rumah tangga di Desa Lingkar Kampus Institut Pertanian Bogor. Desain penelitian ini adalah cross sectional study, melibatkan 90 orang ibu rumah tangga sebagai subjek penelitian. Data yang dikumpulkan meliputi karakteristik contoh, berat dan tinggi badan, lingkar perut, kebiasaan olahraga, kebiasaan merokok, kebiasaan makan, konsumsi pangan, aktivitas fisik, dan penggunaan kontrasepsi hormonal. Pengumpulan data dilakukan dengan wawancara menggunakan kuesioner dan pengukuran langsung. Hasil penelitian menunjukkan bahwa terdapat hubungan signifikan positif antara usia, riwayat obesitas keluarga, frekuensi konsumsi makanan berlemak, lama penggunaan kontrasepsi hormonal, tingkat kecukupan energi, protein, dan lemak contoh dengan obesitas. Terdapat hubungan signifikan negatif antara aktivitas fisik contoh dengan obesitas. Contoh dengan tingkat kecukupan energi yang tinggi memiliki risiko obesitas lebih tinggi dibanding contoh dengan tingkat kecukupan rendah (OR=2.99, 95%CI=1.055-8.490). Contoh yang sering mengonsumsi makanan berlemak (≥7 kali seminggu) memiliki risiko obesitas lebih tinggi dibandingkan contoh yang jarang mengonsumsi makanan berlemak (OR=4.00, 95%CI:1.102-14.520). Contoh yang beraktivitas fisik sedang dan berat memiliki risiko obesitas lebih rendah dibandingkan contoh beraktivitas ringan dan sangat ringan (OR=0.77, 95%CI: 0.019-0.306).
Keyword: aktivitas fisik, faktor risiko, makanan berlemak, obesitas

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