Judul: Faktor-Faktor Yang Mempengaruhi Kejadian Anemia Pada Remaja Dan Dewasa Di Dki Jakarta  Tahun 2007
Abstrak: Anemia is one of the public health problem. Data of RISKESDAS 2007 showed that the prevalence of anemia in DKI Jakarta 2007 is about 15.0 % (14.2% among adolescents and 59.1% among adults), which is higher compared to the national prevalence (11.9%). The objective of this research was to analyze factors that affect anemia among adolescents and adults in Jakarta year 2007. This was a cross-sectional study design, using secondary data of RISKESDAS year 2007. The subjects were 140 adolescents (10-19 yrs) and 627 adults (20-59 yrs). Data was processed by Microsoft Excel 2003 and SPSS program version 13.0. Anemia, defined as haemoglobin concentration <11.5 g/dl (10-11 yrs), <12 g/dl (12-14 yrs), <12 g/dl (women >15 yrs), and <13 g/dl (men >15 yrs) (WHO 2001). The results of a binary logistic regression analysis show that the factors that affected anemia for adolescents is frequently caffeine consumption (OR 0.354). While the factors that affected anemia for adults are women (OR 2.332) and overweight (OR 0.504).
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Judul: Faktor-faktor yang mempengaruhi kejadian anemia pada remaja dan dewasa di DKI Jakarta Tahun 2007
Abstrak: Secara umum penelitian ini bertujuan untuk mengetahui faktor-faktor yang mempengaruhi kejadian anemia pada kelompok remaja dan dewasa di DKI Jakarta tahun 2007. Adapun tujuan khusus dari penelitian ini adalah (1) Mengidentifikasi sebaran contoh anemia pada kelompok remaja di DKI Jakarta, (2) Mengidentifikasi sebaran contoh anemia pada kelompok dewasa di DKI Jakarta, (3) Mengidentifikasi faktor-faktor yang berhubungan dengan kejadian anemia pada kelompok remaja di DKI Jakarta, (4) Mengidentifikasi faktor-faktor yang berhubungan dengan kejadian anemia pada kelompok dewasa di DKI Jakarta, (5) Mengidentifikasi faktor-faktor yang mempengaruhi kejadian anemia pada kelompok remaja di DKI Jakarta, dan (6) Mengidentifikasi faktor-faktor yang mempengaruhi kejadian anemia pada kelompok dewasa di DKI Jakarta. Penelitian ini seluruhnya dilakukan dengan mengolah data dari Riset Kesehatan Dasar (RISKESDAS) 2007 yang berupa data sekunder dengan metode cross-sectional study. Penelitian yang memiliki desain cross sectional study ini dilakukan di Bogor pada bulan Maret hingga Mei 2009. Pemilihan daerah penelitian hanya dipilih untuk wilayah DKI Jakarta dari 33 provinsi di Indonesia. Pengolahan, analisis, dan interpretasi data dilakukan di kampus IPB Dramaga Bogor, Jawa Barat. Umur contoh dibagi menjadi 2 kelompok yakni remaja menurut WHO (1995) berada pada kisaran umur 10-19 tahun, sedangkan kelompok dewasa menurut Ge K et al. (1994) diacu dalam WHO (1995) berada pada kisaran umur 20-59 tahun sehingga besar sampel dengan data lengkap adalah 767 sampel yang meliputi 140 sampel remaja dan 627 sampel dewasa. Data-data yang digunakan dalam penelitian ini seluruhnya merupakan data sekunder wilayah DKI Jakarta tahun 2007 yang diperoleh dari Riset Kesehatan Dasar (Riskesdes 2007), Badan Penelitian dan Pengembangan Kesehatan, Departemen Kesehatan RI. Data terdiri dari data Kesehatan Masyarakat (Kesmas) dan biomedis. Data Kesmas meliputi karakterisik umum contoh (umur, jenis kelamin, tingkat pendidikan, pekerjaan, besar keluarga), status gizi, riwayat diare, dan gaya hidup (perilaku minum alkohol, minuman berkafein, dan konsumsi buah), sedangkan data biomedis berupa kadar hemoglobin untuk mengetahui status anemia contoh. Data yang telah diperoleh dan terkumpul kemudian dianalisis baik secara manual atau dengan menggunakan Microsoft Excel 2003 for Windows dan SPSS 13.0 for Windows dan analisis yang digunakan dalam penelitian ini adalah univariat, bivariat (Chi Square dan korelasi Spearman), dan multivariat (regresi logistik).
Keyword: Adolescents, Adults, Protective factor, Risk factor, Logistic regression, Caffeine

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