Judul: Faktor-faktor yang berhubungan dengan dispepsia pada remaja SMA di Bogor.
Abstrak: Penelitian ini bertujuan untuk mengetahui dan menganalisis faktor-faktor yang berhubungan dengan dispepsia pada remaja SMA di Bogor. Desain penelitian yang digunakan adalah cross sectional dengan metode sampel purposive sampling. Jumlah contoh sebesar 320 siswa/i SMA. Penelitian ini dilakukan pada 5 sekolah di Bogor pada bulan April-Mei 2018. Data yang digunakan dalam penelitian ini sebagian besar menggunakan data primer. Sebanyak 29.7% contoh yang mengalami dispepsia. Terdapat perbedaan yang signifikan (p<0.05) antara contoh dispepsia dan non dispepsia pada jenis kelamin, frekuensi makan, keteraturan makan, jeda waktu makan, sarapan pagi, kebiasaan konsumsi minuman soda, kebiasaan konsumsi makanan pedas, kebiasaan konsumsi makanan asam, dan tingkat stres. Berdasarkan uji korelasi terdapat hubungan yang signifikan (p<0.05) dengan dispepsia yaitu jenis kelamin, frekuensi makan, keteraturan makan, jeda waktu makan, sarapan pagi, kebiasaan konsumsi minuman soda, kebiasaan konsumsi makanan pedas, kebiasaan konsumsi makanan asam, dan tingkat stres. Berdasarkan uji regresi logistik, terdapat faktor-faktor yang mempengaruhi dispepsia pada contoh ialah keteraturan makan (p = 0.035 ; OR = 2.076), jeda makan (p = 0.002 ; OR = 3.027), sarapan pagi (p = 0.006 ; OR = 2.018), kebiasaan makanan pedas (p = 0.000 ; OR = 3.730), kebiasaan minuman soda (p = 0.005 ; OR = 4.081), dan tingkat stres (p = 0.019 ; OR = 2.227).
Keyword: dispepsia, faktor risiko, remaja

Judul: Prevalensi dan Karakteristik Dispepsia pada Mahasiswa Institut Pertanian Bogor.
Abstrak: Penelitian ini bertujuan untuk mengetahui berapa besar prevalensi dispepsia dan karakteristiknya pada mahasiswa Institut Pertanian Bogor (IPB). Desain penelitian ini adalah cross-sectional study dengan jumlah contoh 905 orang. Sebanyak 23.09 % dari contoh teridentifikasi dispepsia fungsional berdasarkan kriteria Roma III. Terdapat perbedaan yang signifikan (p < 0.05) antara dispepsia dengan non dispepsia pada jenis kelamin, keteraturan makan, kebiasaan sarapan, tidak nyaman perut setelah mengonsumsi makan makanan pedas, asam, dan minuman bersoda, serta tingkat stres. Berdasarkan uji korelasi terdapat beberapa variabel yang berhubungan (p < 0.05) dengan dispepsia, yaitu keteraturan makan, kebiasaan sarapan pagi, kebiasaan mengonsumsi kopi, kebiasaan olahraga, dan tingkat stres. Berdasarkan uji regresi logistik, variabel yang berhubungan signifikan adalah jenis kelamin (p = 0.001 ; OR = 2.309), tingkat stres (p = 0.002 ; OR = 2.002), keteraturan makan (p = 0.002 ; OR = 1.822), tidak nyaman di perut setelah mengonsumsi makanan asam (p = 0.011 ; OR = 1.651), Tidak nyaman di perut setelah mengonsumsi minuman bersoda (p = 0.015 ; OR = 1.596), dan tidak nyaman di perut setelah mengonsumsi makanan pedas (p = 0.015 ; OR = 1.579).
Keyword: dispepsia, keteraturan makan, kriteria roma III, tingkat stres

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