Judul: Efek suplementasi multivitamin mineral terhadap kadar hemoglobin dan hematokrit mahasiswi TPB IPB
Abstrak: Anemia merupakan salah satu masalah gizi serius di Indonesia. Prevalensi anemia pada kelompok remaja putri, khususnya siswi masih cukup tinggi (Anggareini 2004; Briawan 2008). Program perbaikan anemia sering dilakukan dalam berbagai penelitian, salah satunya melalui mineral multivitamin suplementasi. Tujuan dari penelitian ini adalah untuk menganalisis pengaruh multivitamin suplementasi mineral terhadap konsentrasi hemoglobin dan hematokrit kota bogor Mahasiswa perempuan Tahun Biasa Pertama Universitas Pertanian. Desain penelitian ini adalah eksperimen semu, buta ganda. Jumlah sampel sebanyak 28 siswi yang selanjutnya dibagi menjadi 2 kelompok. Kelompok intervensi terdiri dari 15 sampel, mendapat suplemen sirup 15 ml per hari selama 8 minggu. Kelompok kontrol terdiri dari 13 sampel yang menerima sirup plasebo. Data dikumpulkan dengan wawancara sampel menggunakan kuesioner, pemeriksaan fisik, dan pemeriksaan biokimia darah. Hasil uji statistik (paired sample t test) menunjukkan tidak terdapat peningkatan kadar hemoglobin dan yang signifikan konsentrasi hematokrit masing-masing pada kelompok intervensi dan kontrol (p>0,05). Namun, pada sebagian sampel anemia, kadarnya meningkat hemoglobin dan hematokrit pada kelompok intervensi cenderung lebih besar dibandingkan kelompok intervensi kelompok kontrol.
Keyword: Anemia, Female students, Multivitamin mineral supplements, Hemoglobin, Hematocrit

Judul: Pengaruh Pendidikan Gizi terhadap Kadar Hemoglobin Mahasiswa Putri TPB IPB yang Diberi Suplementasi Multivitamin Mineral
Abstrak: Anaemia is still prevalence in Indonesia especially in adolescent girls because deficiency of iron and folic acid. The purpose of this study was to analyze the effect of multivitamin minerals supplementation and nutrition education on haemoglobin. The study was conducted on First Common Year Studentsof Bogor Agricultural University. Design of the study was Quasy Experimental with pretest post test group. The number of subjects was 27 and allocated into two groups. First group received multivitamin minerals supplementation without nutrition education (S group); second group received multivitamin and minerals supplementation with nutrition education (SPG group). The method of nutition education was speech and allocated into four session with duration 30-45 minutes for each meeting. The result of the study showed that nutrition education affected nutritional knowledge but could not improve nutritional attitudes and nutritional practice. There was no significant correlation between nutrition knowledge, attitude, and practice. Multivitamin minerals supplementation could not improve haemoglobin, but could increased haemoglobin of anaemia subject although the increase was not significant (p>0.05).
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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