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: . Pengaruh Demografi, Sosial-Ekonomi, Gaya Hidup, Serta Gizi Dan Kesehatan Terhadap Kejadian Obesitas Sentral Pada Ibu Rumah Tangga.
Abstrak: Penelitian ini bertujuan untuk menganalisis faktor-faktor yang berpengaruh terhadap kejadian obesitas sentral pada ibu rumah tangga di Desa Tanjungjaya, Kecamatan Tanjungjaya, Kabupaten Tasikmalaya. Desain penelitian ini adalah cross sectional dengan metode wawancara menggunakan kuesioner. Penelitian ini dilakukan pada 96 contoh ibu rumah tangga pada bulan April sampai dengan Juni 2014. Analisis data yang dilakukan meliputi uji deskriptif, uji korelasi Spearman, uji korelasi Kontingensi dan uji regresi logistik biner metode forward stepwise. Persentase obesitas sentral pada contoh ditemukan sebesar 69.8%. Sebagian besar contoh (63.5%) memiliki pendapatan per kapita di atas garis kemiskinan. Aktivitas fisik contoh sebagian besar (84.4%) termasuk ke dalam kategori ringan. Konsumsi buah dan sayur sebanyak 97.9% masih berada di bawah rekomendasi Pedoman Gizi Seimbang. Sebanyak 51% contoh mengonsumsi makanan jajanan sedikitnya satu kali per hari. Tekanan darah contoh rata-rata (62.5%) berada pada kategori prehipertensi. Terdapat hubungan yang signifikan (p<0.05) antara pendapatan per kapita, konsumsi sayur, dan status gizi dengan obesitas sentral. Pendapatan per kapita per bulan yang berada di bawah garis kemiskinan merupakan faktor penghambat kejadian obesitas sentral pada contoh (OR=0.397).
Keyword: faktor risiko, ibu rumah tangga, obesitas sentral

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