Judul: Hubungan Pengetahuan, Sikap, dan Praktek Gizi pada Ibu Hamil di Desa Cikeas Kecamatan Sukaraja Bogor
Abstrak: The objective of this research was to analyze the correlations between knowledge, attitude, and nutritional practice of pregnant women at Cikeas Village Sukaraja Bogor. The design of the research was a cross sectional study. Total sample was 40 respondents selected by purposive sampling. Primary data consisted of pregnant women characteristics, knowledge, attitude, nutrition practice, and iron intake. The results showed that there were significant correlations between education level and nutritional knowledge (r=0.457, p<0.01), nutritional knowledge and nutritional attitude (r= r=0.410, p<0.01). This research showed inadequate intake of energy, protein, and iron intake from food among pregnant women. There was no significant correlation between nutritional knowledge and nutritional attitude with iron adequate level from food and iron suplement.
Keyword: 

Judul: Kebiasaan Makan Wanita Hamil Dan Ibu Menyusui Di Desa Girijaya Kecamatan Sindangbarang Kabupaten Cianjur Jawa Barat
Abstrak: Tujuan penelitian ini adalah untuk mengetahui hubungan pendidikan terhadap pengetahuan gizi wanita hamil dan ibu menyusui, hubungan pengetahuan gizi terhadap frekuensi kon- sumsi wanita hamil dan ibu menyusui dan pandangan mengenai makanan pantangan wanita hamil dan ibu menyusui pada kelom- pok ibu yang belum, sedang dan pernah hamil serta menyusui. Responden yang diteliti sebanyak 60 orang yang terdiri dari 10 wanita belum hamil, 30 wanita hamil, 10 ibu menyusu- i dan 10 ibu pernah hamil. Pengambilan responden untuk masing-masing kelompok dilakukan secara Quota Sample', yaitu responden dipilih tanpa patokan tertentu (Yacob Vredenbergt, 1978). Responden didapatkan peneliti dari keterangan ibu- ibu PKK dan para ketua RT. Data diperoleh dengan wawancara. Pada kelompok responden yang belum pernah hamil terda- pat 5 orang yang berpengetahuan gizi dan 5 orang yang tidak berpengetahuan gizi. Pada kelompok responden yang berpengetahuan gizi dari kelompok yang belum pernah hamil, yang berpendidikan lebih tinggi yaitu kelas 2 SMP dan tamat SPG pengetahuan gizinya lebih baik daripada responden yang pendidikannya hanya sampai tingkat Sekolah Dasar. Menurut responden yang pendidik- annya lebih tinggi bahwa pada saat hamil dan menyusui seseorang perlu banyak makan ikan, daging, sayuran dan buah- buahan. Sedang yang berpendidikan SD mengatakan perlu ba- nyak makan sayuran atau buah-buahan saja. Responden yang pendidikannya lebih tinggi lebih mampu menangkap informasi pengetahuan gizi yang sumbernya berasal dari sekolah, per- kumpulan ibu-ibu, radio, TV dan koran. Pada kelompok wanita sedang hamil terdapat 17 orang tidak berpengetahuan gizi dan 13 orang berpengetahuan gizi. Responden yang berpengetahuan gizi dari kelompok tersebut, terdapat 2 orang berpendidikan SD yang pengetahuan gi- zinya lebih baik daripada yang berpendidikan SD lainnya. Seorang dari kedua orang tersebut mendapatkan pengetahuan gizi waktu ia tinggal di Bandung, dan seorang lagi mendapatkannya dari banyak sumber seperti dari radio, TV, klinik dan perkumpulan ibu-ibu. Dua orang tersebut mengatakan bahwa pada saat hamil dan menyusui perlu banyak makan ikan, daging, telur dan sayuran. Sedang responden yang berpendidikan SD lainnya mengatakan perlu banyak makan buah-buahan, sa- yuran dan kacang-kacangan saja…dst
Keyword: 

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