Judul: Faktor-faktor yang berhubungan dengan konsumsi pangan mahasiswa putri yang anemia dan non-anemia
Abstrak: The objectives of this research were to describe food consumption and related factors of anaemic and non-anaemic female college students. The research was a cross-sectional study and conducted in IPB’s Female Dormitory from September 2005 until March 2006. The samples were female college students who stayed at IPB’s Female Dormitory and joined IPB’s Feeding Program. There were 60 samples divided into 2 groups, anaemic group (Hb < 12 g/ dL) and non-anaemic group (Hb > 12 g/ dL). Food consumption analysis based on food frequency questionnaire and 3x24 hours food consumption recall. Study results showed that kinds of food (defined as food that were consumed > 4 times in a week by > 50% of the sample) in anaemic group were rice and tempeh, and in non-anaemic group were rice, tempeh and egg. The non-anaemic group had higher frequency in consumption of carbohydrate-food, animal protein and milk. The daily energy and protein intake in both groups were low, the iron and vitamin C intake were very low and the vitamin A intake was adequate. However, the daily intake of protein and iron in non-anaemic group was higher than it in anaemic group.
Keyword: 

Judul: Hubungan Pola Konsumsi Pangan Inhibitor dan Enhancer Fe, Bioavailabilitas Fe dan Status Gizi dengan Status Anemia Mahasiswi IPB
Abstrak: Penelitian ini bertujuan mengidentifikasi dan menganalisis hubungan pola konsumsi pangan inihibitor dan enhancer Fe, bioavailabilitas Fe, status gizi dengan status anemia mahasiswi IPB. Studi cross sectional dilakukan pada mahasiswi gizi IPB. Data primer dikumpulkan dari 69 orang mahasiswi meliputi pola konsumsi pangan sumber heme dan nonheme, intake zat gizi makro dan mikro berdasarkan FFQ dan konsumsi pangan recall 2x24 jam, data kadar hemoglobin, status gizi, serta bioavailabilitas Fe. Pengukuran bioavailabilitas Fe menggunakan metode Du et al. (2000. Hasil penelitian menunjukkan persentase anemia responden sebesar 56.5% termasuk kategori anemia ringan. Lebih dari separuh responden berstatus gizi normal. Konsumsi kelompok pangan inhibitor meliputi konsumsi pangan serealia (322.9 g/hari), kacang-kacangan (62.6 g/hari), dan teh (2.7 g/hari). Kelompok pangan enhancer yaitu sayuran (59.1 g/hari) dan buah (96.3 g/hari). Rerata tingkat kecukupan gizi berturut turut energi (55.1%), protein (69.3%), Fe (32.7%), Vitamin A (185%), vitamin C (38.7%) dan kalsium (27.7%). Hasil uji korelasi Rank Spearman, menunjukkan ada hubungan frekuensi konsumsi enhancer tomat dan suplemen dengan status anemia (p<0.05). Ada hubungan negatif antara konsumsi pangan inhibitor Fe yaitu kacang-kacangan, kopi dan susu (p<0.05). Ada hubungan konsumsi pangan enhancer Fe yaitu vitamin C, pangan hewani dan sayuran dengan status anemia (p<0.05), namun tidak terdapat hubungan yang signifikan antara status gizi dan bioavailabilitas Fe dengan status anemia (p>0.05).
Keyword: anemia, bioavailabilitas Fe, enhancer, inhibitor, status gizi

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