Judul: Faktor-Faktor yang Berhubungan dengan Kejadian Dismenorea Primer pada Remaja
Abstrak: Penelitian ini bertujuan untuk mempelajari faktor-faktor yang berhubungan dengan kejadian dismenorea primer pada remaja. Desain penelitian yang digunakan dalam penelitian ini adalah cross sectional study dengan jumlah subjek sebanyak 85 orang. Subjek dalam penelitian ini adalah siswi kelas X dan XI SMA Kornita Bogor. Penelitian dilakukan pada bulan April – Mei 2016. Data yang dikumpulkan meliputi karakteristik subjek, karakteristik menstruasi, status gizi, aktivitas fisik, keluhan menstruasi, dan asupan energi dan zat gizi. Hasil penelitian menunjukkan sebagian besar (74.1%) subjek penelitian mengalami dismenorea primer. Berdasarkan uji regresi logistik bivariat diketahui bahwa status gizi lebih (p-value 0.803 OR=1.157 95% Cl 0.368-3.645), usia menarche dini (p-value 0.083 OR=2.4 95% Cl 0.892-6.454), tingkat kecukupan vitamin B1 (p-value 0.197 OR=2.0 95% Cl 0.698-5.729), dan kalsium (p-value 0.765 OR=1.452 95% 0.125- 16.850) yang kurang belum menjadi faktor risiko terjadinya dismenorea primer namun sudah berpeluang meningkatkan kejadian dismenorea primer.
Keyword: aktivitas fisik, dismenorea primer, status gizi, tingkat kecukupan gizi

Judul: Faktor-faktor yang Berhubungan dengan Kejadian Dismenore Primer pada Mahasiswi Institut Pertanian Bogor
Abstrak: Dismenore primer merupakan gangguan saat menstruasi yang dialami oleh banyak wanita, termasuk mahasiswi. Penelitian ini bertujuan menganalisis faktorfaktor yang berhubungan dengan kejadian dismenore primer pada mahasiswi Institut Pertanian Bogor (IPB). Desain penelitian ini adalah cross sectional study, melibatkan 97 subyek. Pengambilan data dilakukan secara online melalui pengisian kuesioner Google Form (data karakteristik dan aktivitas fisik subyek) dan wawancara melalui voice call (data riwayat asupan). Sekitar 2/3 subyek (69,1%) mengalami dismenore primer terutama tingkat 1 (49,5%). Sekitar 1/3 subyek (27,8%) memiliki ibu yang mengalami dismenore. Hampir 2/3 subyek yang mengalami dismenore primer (61,2%) merasa terganggu aktivitas belajarnya saat mengalami dismenore. Sekitar separuh subyek (51,5%) memiliki asupan energi kurang, 4/5 subyek (81,4%) memiliki kecukupan karbohidrat kurang dan hampir separuh subyek (47,4%) memiliki kecukupan lemak lebih. Sebagian besar subyek memiliki asupan zat gizi mikro kurang kecuali vitamin A. Terdapat hubungan yang signifikan antara dismenore primer dengan riwayat dismenore ibu (p<0,05). Terdapat hubungan yang signifikan antara nilai status gizi dengan jumlah asupan energi (p=0,007, r=-0,271) dan karbohidrat (p=0,005, r=-0,283). Terdapat hubungan yang signifikan antara gangguan belajar akibat dismenore primer dengan tingkat intensitas dismenore (p=0,000).
Keyword: female college students, menstruation, nutrients intake, physical activity, primary dysmenorrhea

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