Judul: Pengetahuan Gizi, Keluhan Kesehatan, Kondisi Psikologis, dan Pola Pemberian Air Susu Ibu (ASI) Ibu Postpartum
Abstrak: Postpartum period is restoration period after childbirth with one of characteristics is the process of breastfeeding. This study was aimed to assess of nutritional knowledge, health complaints, psychological conditions, and their association with breastfeeding pattern on postpartum mother. A cross sectional study design and interview method was conducted in five rural areas in Dramaga Subdistrict, Bogor District, during the period of April to May 2013. Sample included in this study were 30 postpartum mothers who met inclusion criteria sample is the age of a baby 10−40 days and baby was not a first childbirth, she will be the respondent through the informed consent agreement, and be able communicate. The mayority postpartum mother had level of nutritional knowledge (60%) and health complaints (66.7%) were classified in low, and they have depression (56.7%). The nutritional knowledge of exclusive breastfeeding tends to correlate (r=0.126, p=0.508) exclusive breastfeeding practice. Meanwhile, lower health complaint tends to correlate with better breastfeeding practices.
Keyword: postpartum mother, breastfeeding, psychological, health complaints, Nutritional knowledge

Judul: Pengetahuan, Sikap, Dan Praktek Pemberian Asi Serta Status Gizi Bayi Usia 4-12 Bulan Di Perdesaan Dan Perkotaan
Abstrak: Exclusive breastfeeding rates in Indonesia is still low. Some studies indicated that low mothers' nutritional knowledge and attitude is the cause of low exclusive breastfeeding practice. The main aim of this study was to compare breastfeeding knowledge, attitude, practice and infants nutritional status between rural and urban areas. In this cross-sectional study, Desa Jayabakti, Kabupaten Sukabumi represents rural area, while Kelurahan Kedung Jaya, Kota Bogor represents urban area. Samples were 31 mothers-infants in each area who were selected by simple cluster sampling method. The differences between two areas were analyzed with the independent t-test, chi-square test, and Fisher's exact test. The correlation between variables were analyzed with rank Spearman. The result of this study showed that mother's knowledge and attitude of nutrition, especially about exclusive breastfeeding, was higher in urban than in rural area. There were differences in mothers' knowledge and attitude between rural and urban areas (p<0.05). Exclusive breastfeeding in rural area practiced by 41.9%, which is higher than in urban area (25.8%). Nevertheless, there was no statistical difference in exclusive breastfeeding practice between rural and urban areas (p>0.05). Breastfeeding practices which statistically difference in rural and urban areas were the introduction of colostrum status and brastfeeding time (p<0.05). Early initiation of breastfeeding was associated with exclusive berastfeeding practice in urban area (p<0.05), but none of the variables were associated with exclusive breastfeeding practice in rural area (p>0.05). Furthermore, exclusive breastfeeding practice was not associated with 4-12 months infants nutritional status (p>0.05). Mothers' family should be targeted as target of audience in breastfeeding promotion.
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