Judul: Kebiasaan minum, kebutuhan cairan dan kecenderungan dehidrasi siswa sekolah dasar
Abstrak: Tujuan umum dari penelitian ini adalah mengetahui kebiasaan minum dan kebutuhan cairan serta status hidrasi pada siswa SD Polisi 4 Begor. Adapun tujuan khususnya adalah (1) Mengetahui kebiasaan minum siswa, (2) Mengetahui intake cairan siswa, (3) Mengetahui kebutuhan cairan siswa, (4) Mengetahui status hidrasi siswa (5) Menganalisis hubungan persentase tingkat konsumsi cairan dengan status hidrasi siswa, (6) Menganalisis hubungan intake energi dengan persentase tingkat konsumsi cairan pada siswa. Desain penelitian ini adalah cross sectional study. Penelitian dilakukan dengan wawancara serta pengisian lembar recall dan FFQ (Food Frequency Questionaire) di SD Polisi 4 Begor. Penentuan sekolah yang dijadikan lokasi penelitian dilakukan secara purposive. Conteh penelitian ini adalah adalah siswa kelas 4 dan 5 SD Polisi 4 Begor dengan kriteria sehat (tidak sedang menderita penyakit diare, ginjal, demam berdarah, serta radang tenggorokan). Jumlah populasi adalah 169. Jumlah contoh sebanyak 86 siswa yang dihitung menggunakan formula estimasi of mean. Jenis data yang dikumpulkan berupa data primer dan data sekunder. Data primer meliputi data karakteristik contoh (umur, BB, TB dan jumlah uang saku untu pengeluaran pangan). Data karakteristik sosial ekonomi keluarga contoh meliputi besar keluarga, pekerjaan ayah dan pendidikan ayah. Data sekunder meliputi pendidikan ayah dan pekerjaan ayah diperoleh dari database yang terdapat di sekolah. Data kebiasaan minum contoh diperoleh dari FFQ (Food Frequency Questionaire). Kebiasaan minum di sekolah diperoleh melalui wawancara langsung dengan contoh. Data intake cairan merupakan total intake cairan dari makanan dan minuman. Kecenderungan dehidrasi dilihat dari tanda-tanda dehidrasi antara lain haus, lelah, kulit kering, mulut dan tenggorokan kering (Asian Food Information Centre 2000). Kebutuhan cairan contoh dihitung dengan rumus Grant & DeHoog (1999) yang diacu dalam Mahan K. & Escott- Stump (2004) serta berdasarkan rekomendasi dari The National Research Council (NRC) diacu dalam Sawka M el al. (2005). Hubungan antara persentase tingkat konsumsi cairan dengan kecenderungan dehidrasi dianalisis menggunakan Uji Chi Square, hubungan antara intake energi dengan persentase / tingkat konsumsi cairan dianalisis menggunakan Uji Korelasi Pearson. ...
Keyword: School children, Fluid intake, Sign of dehydration

Judul: Kebiasaan Minum, Kebutuhan Cairan Dan Kecenderungan Dehidrasi Siswi Sekolah Dasar
Abstrak: The objective of this research was to assess drinking habbit, fluid requirement and sign of dehydration among female elementary school student. Cross sectional study was done from May to June 2009 in Polisi 4 Elementary School Bogor. The samples were student at grade 4 and 5 and healthy as an inclusive criteria. The number of sample was calculated by mean estimation. The average fluid intake from food and beverage are 2024,4 ± 287,4 ml/day. The average fluid intake from food is 426,6 ± 126 ml/day and from beverage is 1597,8 ± 243 ml/day. The average fluid requirement of sample based on Grant & DeHoog (1999) in Mahan K. & Escott-Stump (2004) is 1789,7 ± 158,1 ml. Based on sign of dehydration, there is 62,8% of samples haved mild dehydration. The level of fluid consumption doesn’t have significant correlation with the trend of dehydration. Energy intake has significant correlation with the level of fluid consumption (r=0,322 ; p<0,01).
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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