Judul: Hubungan Asupan Air, Status Hidrasi, Dan Kemampuan Kognitif Pada Wanita Usia Lanjut Di Panti Werdha Jakarta Selatan
Abstrak: Penelitian ini bertujuan untuk menganalisis hubungan antara asupan air, status hidrasi, dan kemampuan kognitif pada wanita usia lanjut di Panti Werdha, Jakarta Selatan. Desain penelitian yang digunakan adalah cross sectional study dengan jumlah subjek 40 orang. Penelitian dilaksanakan pada bulan Februari-Juli 2016. Asupan air terdiri dari air makanan, air minuman, dan air metabolik. Air makanan dan air minuman didapatkan dengan metode food recall 3x24 jam. Status hidrasi ditetapkan berdasarkan nilai berat jenis urin (USG). Kemampuan kognitif diukur menggunakan kuesioner Mini-Mental State Examination (MMSE). Hasil penelitian menunjukkan bahwa rata-rata asupan air subjek adalah 1922.4±430.6 mL/hari. Sebagian besar tingkat kecukupan air subjek termasuk kategori berlebih. Sebanyak 42.5% subjek mengalami dehidrasi dan 25% subjek mengalami gangguan kognitif. Hasil uji korelasi menunjukkan tidak terdapat hubungan yang signifikan antara asupan air dan status hidrasi dengan kemampuan kognitif (p>0.05).
Keyword: asupan air, kognitif, status hidrasi, wanita usia lanjut.

Judul: Hubungan Asupan Air, Status Hidrasi dan, Kemampuan Kognitif pada Wanita Usia Lanjut di Panti Werdha Jakarta Barat
Abstrak: Tujuan dari penelitian ini adalah untuk menganalisis hubungan asupan air, status hidrasi, dan kemampuan kognitif pada wanita usia lanjut di Panti Werdha Jakarta Barat. Desain penelitian berupa cross sectional study dengan subjek sebanyak 41 orang. Asupan air terdiri dari air makanan, air minuman dan air metabolik. Konsumsi makanan dan minuman didasarkan pada metode food recall 3x24 jam. Status hidrasi didapatkan berdasarkan metode berat jenis urin. Kemampuan kognitif dinilai dengan menggunakan kuesioner Mini-Mental State Examination (MMSE). Hasil penelitian menunjukkan bahwa rata-rata total asupan air pada subjek sebesar 1707.9 ± 490.7 mL/hari. Rata-rata tingkat kecukupan air subjek yaitu 121.8 ± 35.7%. Sebanyak 19.5% subjek mengalami dehidrasi berdasarkan metode berat jenis urin secara berturut-turut. Sebanyak 24.4% subjek mengalami gangguan kognitif. Hasil uji Pearson dan Spearman menunjukkan tidak terdapat hubungan nyata antara asupan air, status hidrasi, dan kemampuan kognitif (p>0.05).
Keyword: asupan air, kognitif, status hidrasi, wanita usia lanjut

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