Judul: Hubungan Kebiasaan Sarapan, Status Hidrasi, dan Status Anemia dengan Konsentrasi Belajar Siswi MAN 1 Kota Bandung.
Abstrak: Penelitian ini bertujuan untuk menganalisis hubungan kebiasaan sarapan, status hidrasi, dan status anemia dengan konsentrasi belajar subjek. Status hidrasi diukur menggunakan warna urin, status anemia diukur melalui pengukuran kadar hemoglobin darah metode Cyanmethemoglobin, dan konsentrasi belajar diukur menggunakan instrumen Digit Symbol Substitution Test (DSST). Desain penelitian yang digunakan adalah cross sectional study. Sebanyak 57 siswi di MAN 1 Kota Bandung menjadi subjek yang dipilih dengan teknik simple random sampling. Hasil penelitian menunjukkan bahwa sebagian besar subjek memiliki kebiasaan sarapan yang rutin (89.5%) dan sebelum pukul 09.00 (80.7%), namun tingkat kecukupan zat besi sarapan sebagian besar (89.5%) subjek tergolong rendah. Ratarata asupan cairan subjek 1600.02 ml dan sebagian besar subjek (63.2%) memiliki tingkat kecukupan cairan yang kurang. Sebanyak 36.8% subjek mengalami anemia dan hampir setengah dari subjek penelitian tergolong berisiko dehidrasi (38.6%) serta mengalami dehidrasi (22.8%). Konsentrasi belajar sebagian besar subjek (52.6%) tergolong kurang. Terdapat hubungan yang signifikan (p<0.05) antara status hidrasi dan status anemia subjek dengan konsentrasi belajar. Tingkat kecukupan zat gizi sarapan berhubungan signifikan dengan konsentrasi belajar (p<0.05), namun waktu dan frekuensi sarapan tidak berhubungan dengan konsentrasi belajar.
Keyword: kebiasaan sarapan, konsentrasi, remaja, status anemia, status hidrasi

Judul: Hubungan Kebiasaan Sarapan dan Status Hidrasi dengan Konsentrasi Belajar Mahasiswa Gizi IPB
Abstrak: Penelitian ini bertujuan untuk menganalisis hubungan kebiasaan sarapan dan status hidrasi dengan konsentrasi belajar mahasiswa gizi IPB. Desain penelitian ini adalah cross sectional study dengan jumlah subjek berjumlah 63 orang yang dipilih secara purposive sampling. Kebiasaan sarapan sebagian besar subjek tergolong rutin sarapan (58,7%) dan tingkat kecukupan energi, protein, lemak dan karbohidrat sarapan tergolong kurang. Rata-rata tingkat kecukupan cairan subjek tergolong normal dan status hidrasi sebagian besar subjek tergolong terhidrasi dengan baik (61,9%). Konsentrasi belajar sebagian besar subjek tergolong baik (55,6%). Tidak ada perbedaan signifikan antara kebiasaan sarapan dan tingkat kecukupan cairan laki-laki dan perempuan (p>0,05). Usia, status gizi, pendidikan orang tua, pendapatan orang tua, dan besar keluarga tidak ada hubungan dengan kebiasaan sarapan dan tingkat kecukupan cairan (p>0,05), namun besar keluarga berhubungan signifikan secara negatif dengan frekuensi sarapan (p=0,03;r=-0,269). Pengetahuan gizi subjek tidak ada hubungan dengan kebiasaan sarapan dan tingkat kecukupan cairan (p>0,05). Terdapat hubungan signifikan secara positif antara tingkat kecukupan energi (p=0,007;r=0,335), protein (p=0,032;r=0,270), lemak (p=0,016;r=0,302), dan karbohidrat (p=0,16;r=0,304) sarapan dan konsentrasi belajar, namun waktu dan frekuensi sarapan tidak berhubungan dengan konsentrasi belajar (p>0,05). Tidak terdapat hubungan signifikan antara status hidrasi dengan konsentrasi belajar (p=0,582;=r-0,71)., This study aimed to analyze the correlation between breakfast habits and hydration status with learning concentration among nutrition students at IPB University. Design of this study was cross sectional study with 63 subjects who were selected by purposive sampling. Breakfast habits in most subjects were classified as having breakfast regularly (58,7%) and had a deficit energy, protein, fat and carbohydrate adequacy level. The average level of the subject’s fluid adequacy was classified as adequate and hydration status in most of subjects were classified as hydrated (61,9%). Learning concentration in most of subjects were classified as good (55,6%). There were no significant difference between breakfast habits and fluid adequacy level among male and female (p>0,05). Age, nutritional status, parent education, family income, dan family size have no significant correlation with breakfast habits and fluid adequacy level (p>0,05), but family size have negatively significant correlation with breakfast frequency (p=0,03;r=-0,269). Nutritional knowledge has no correlation with breakfast habits and fluid adequacy level (p>0,05). There were positively significant correlation between energy (p=0,007;r=0,335), protein (p=0,032;r=0,270), fat (p=0,016;r=0,302), dan carbohydrate (p=0,16;r=0,304) adequacy level and learning concentration, but breakfast time and frequency have no correlation with learning concentration. There was no significant correlation between hydration status with learning concentration (p=0,582;=r-0,71).
Keyword: Breakfast habit, college students, hydration status, learning concentration

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