Judul: Screen time, stres akademik, dan konsumsi pangan serta pengaruhnya terhadap status gizi mahasiswa TPB IPB
Abstrak: Penelitian ini bertujuan menganalisis screen time, stres akademik, dan konsumsi pangan serta pengaruhnya terhadap status gizi mahasiswa TPB Institut Pertanian Bogor. Desain penelitian adalah cross-sectional study secara purposive sebanyak 68 contoh terdiri dari 34 contoh normal dan 34 contoh obes. Rata-rata screen time pada contoh obes (5.76 ± 2.47) jam/hari lebih tinggi dibandingkan contoh normal (2.71 ± 1.54) jam/hari. Proporsi tingkat stres tertinggi pada contoh normal (76.47%) dan contoh obes (47.06%) tergolong kategori sedang. Hasil uji beda Mann Whitney menunjukkan terdapat perbedaan yang nyata (p<0.05) antara frekuensi konsumsi makanan dan minuman manis kedua contoh. Hasil uji beda Independent Sample T-test menunjukkan terdapat perbedaan yang nyata (p<0.05) antara screen time, stres akademik, frekuensi konsumsi makanan berlemak, fast food, dan tingkat kecukupan energi. Hasil uji regresi linear berganda menunjukkan bahwa faktor yang mempengaruhi status gizi contoh adalah screen time, stres akademik, frekuensi konsumsi makanan dan minuman manis, makanan berlemak, dan tingkat kecukupan energi.
Keyword: Academic stress, Food consumption, Nutritional status, Screen time, Human ecology, Cross sectional study, Sweetened foods, Energy adequacy, Multiple linear regression

Judul: Konsumsi Pangan, Status Gizi, Akivitas Fisik, Status Kesehatan dan Status Hidrasi Mahasiswa TPB IPB Angkatan 2014.
Abstrak: Penelitian ini bertujuan untuk menganalisis konsumsi pangan, status gizi, aktivitas fisik, status kesehatan dan status hidrasi mahasiswa TPB IPB angkatan 2014 dengan desain penelitian cross sectional study. Jumlah subjek dalam penelitian ini 97 mahasiswa. Hasil penelitian menunjukkan bahwa sebagian besar subjek memiliki tingkat kecukupan zat gizi termasuk defisit berat dan tingkat kecukupan air termasuk kurang. Subjek dengan status gizi normal sebanyak 71.1%. Selanjutnya juga diketahui bahwa sebesar 35.1% subjek mempunyai status hidrasi kurang dan sebesar 10.3% subjek mempunyai status hidrasi sangat kurang. Tidak ada hubungan yang signifikan antara status gizi dengan status hidrasi dan tingkat kecukupan zat gizi (p>0.05). Terdapat hubungan yang signifikan antara status gizi dengan persen lemak tubuh (p=0.000, r= 0.711). Terdapat hubungan yang signifikan antara status hidrasi dengan tingkat kecukupan air berdasarkan kebutuhan air yang dihitung dengan rumus The National Research Council (NRC) (p=0.018, r=-0.240). Tidak terdapat hubungan yang signifikan antara status hidrasi dengan tingkat kecukupan air berdasarkan kebutuhan air yang dihitung dengan rumus Body Surface Area (BSA) dan berat badan (p>0.05).
Keyword: air, hidrasi, status gizi, zat 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