Judul: Densitas Gizi Konsumsi Pangan Remaja di Kota Bandung dan Yogyakarta.
Abstrak: Tujuan penelitian adalah menganalisis densitas gizi remaja di Bandung dan Yogyakarta dan hubungannya dengan indeks massa tubuh (IMT) dan status anemia. Desain penelitian merupakan desain cross sectional melibatkan 184 contoh (14-17 tahun). Penelitian menggunakan data sekunder dari penelitian Pengembangan Model Pendidikan Gizi Berbasis Web untuk Perbaikan Perilaku Makan Remaja (Dwiriani et al. 2013) dengan menggunakan data karakteristik contoh dan sosial ekonomi, antropometri, kadar hemoglobin, dan data recall 1x24 jam. Hasil penelitian menunjukan densitas energi konsumsi contoh Bandung 2.0 ± 0.4 kkal/g dan contoh Yogyakarta 1.9 ± 0.35 kkal/g dengan densitas asupan protein tergolong cukup, densitas asupan 4 zat gizi mikro kurang kecuali densitas zat besi pada contoh Yogyakarta. Terdapat hubungan positif (p<0.05) antara densitas asupan zat gizi dengan usia, jenis kelamin, pendidikan ibu, pekerjaan ayah, IMT, dan status anemia dan hubungan negatif (p<0.05) antara densitas asupan zat gizi dengan besar keluarga. Pangan dengan kualitas terbaik berdasarkan densitas energi pangan dan indeks Nutrient Rich Food (NRF) 9.3 adalah buah-buahan, sayuran daun, telur, dan pangan nabati. Jajanan merupakan pangan yang tinggi keragamannya namun rendah zat gizi.
Keyword: densitas energi, densitas zat gizi, Nutrient Rich Food Index, remaja

Judul: Healthy Eating Index Remaja di Kota Yogyakarta dan Padang
Abstrak: Remaja rentan terhadap masalah kekurangan maupun kelebihan gizi. Salah satu faktor penyebab masalah tersebut adalah kualitas konsumsi pangan yang dapat diukur dengan healthy eating index (HEI) dan mean adequacy ratio (MAR). Tujuan penelitian ini adalah mempelajari hubungan karakteristik sosial ekonomi keluarga dengan kualitas konsumsi pangan remaja di Yogyakarta dan Padang. Data konsumsi diperoleh dari recall 1x24 jam. Skor MAR termasuk kategori kurang (61.8 %), sedang (21.1 %), dan baik (17.1 %). Skor HEI konsumsi contoh termasuk kategori buruk (77.9 %). Hasil uji hubungan menunjukkan bahwa terdapat hubungan signifikan positif antara pendidikan ayah dan ibu dengan skor HEI dan MAR (p<0.05), hubungan signifikan positif antara pendidikan ayah dan ibu (p<0.05), dan hubungan signifikan antara sumber karbohidrat dengan status gizi IMT/U (p<0.05).
Keyword: healthy eating index, kualitas konsumsi, mean adequacy ratio

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