Judul: Hubungan Pola Konsumsi dan Aktivitas Fisik dengan Kekuatan dan Daya Tahan Taruna Akademi Imigrasi Depok, Jawa Barat
Abstrak: Strength and endurance are the important components of fitness required by to Taruna of Immigration Academy in their education. Both components are able to support learning and physical activity of taruna. This study was aimed to observe the association between consumption pattern and physical activity to strength and endurance of taruna in Immigration Academy, Depok, West Java. A cross sectional study of 63 taruna was conducted. The study showed that there was significant correlation (p<0.05) between nutritional status and strength of taruna. There is no significant correlation (p>0.05) between adequacy levels of energy, protein, fat, carbohydrate, calcium, iron, vitamin A, vitamin B1, vitamin C, physical activity with strength and endurance. There was no significant correlation (p>0.05) between nutritional status and endurance of taruna.
Keyword: endurance, consumption pattern, physical activity, strength

Judul: Hubungan konsumsi pangan, aktivitas fisik, dan keseimbangan energi dengan ketahanan fisik Taruna Akademi Angkatan Udara Yogyakarta
Abstrak: Tujuan dari penelitian ini adalah untuk menganalisis hubungan antar makanan konsumsi, aktivitas fisik, dan keseimbangan energi dengan ketahanan fisik Udara Mahasiswa Akademi Angkatan di Yogyakarta. Penelitian ini dilakukan pada bulan Januari hingga Februari 2012 dengan menggunakan desain studi cross-sectional. Acak Berstrata Smapling sampel terpilih (n=70) terdiri dari 33 sampel pada level II dan 37 sampel pada tingkat IV. Data yang digunakan adalah data primer (karakteristik sampel, pengukuran data antropometri dan aktivitas fisik sampel) dan data sekunder (formal kegiatan sampel, daftar menu yang disajikan oleh asrama, dan skor ketahanan fisik sampel dari hasil Cooper tes yang diselenggarakan oleh Akademi Angkatan Udara). Data dianalisis oleh Pearson Uji korelasi antara status gizi dengan ketahanan fisik menunjukkan tidak negatif signifikan pada sampel level II (p=0,496, r=-0,123) dan juga pada sampel tingkat IV (p=0,243, r=-0,197). Antara tingkat kecukupan energi dengan fisik daya tahan menunjukkan positif tidak signifikan pada sampel level II (p= 0.954, r=0.013) dan negatif tidak signifikan pada sampel level IV (p=0.466, r=-0.124). Di antara Aktivitas fisik dengan ketahanan fisik menunjukkan pengaruh positif tidak signifikan sampel level II (p=0.765, r=0.54) dan tidak negatif signifikan pada level sampel IV (p=0,093, r=-0,280). Berdasarkan penelitian ini, sampel harus diperhatikan pengaturan pangan guna mengoptimalkan asupan gizi yang diperlukan untuk mencapainya ketahanan fisik yang optimal.
Keyword: Food consumption, Physical activity, Physical endurance, VO2max

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