Judul: Kadar dan Ketersediaan Zat Besi Menu Tradisional Asal Nusa Tenggara Barat, Sulawesi dan Timor-Timur
Abstrak: Menu tradisional adalah hidangan makanan keluarga yang diterima dan dimanfaatkan secara turun-temurun, dibatasi oleh wilayah etnik dan mempunyai ikatan sosial yang di dalamnya terdapat aturan termasuk pantangan tertentu. Menu tradisional merupakan potensi daerah yang perlu dikembangkan untuk menunjang upaya preventif mengatasi masalah anemi dalam rangka meningkatkan status kesehatan masyarakat khususnya meningkatkan asupan zat besi. Bertolak dari hal di atas, peneliti tertarik untuk mempelajari nilai glZI menu tradisional melalui analisis energi (diperoleh dari hasil perhitungan analisis protein, karbohidrat dan lemak), karbohidrat (by difference), protein, lemak, kadar air, kadar abu, vitamin C, total zat besi, ketersediaan zat besi, asam fitat dan serat (lignin).
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Judul: Bioavailabilitas Zat Besi dan Seng pada Makanan Jajanan Tradisional Berbahan Dasar Terigu
Abstrak: Penelitian ini bertujuan untuk menganalisis bioavailabilitas zat besi dan seng pada makanan jajanan tradisional berbahan dasar terigu. Tahap pertama, dilakukan survey pasar di Pasar Bogor dan Pasar Anyar untuk menetapkan jenis makanan jajanan yang dijadikan sampel penelitian, dan selanjutnya dilakukan penelitian utama yang meliputi analisis proksimat, analisis kadar zat besi dan seng, serta analisis bioavailabilitas dua mineral tersebut. Hasil survey menunjukkan bahwa makanan jajanan tradisional berbahan dasar terigu yang banyak dijual terdiri atas kue bolu kukus, bolu sakura, putu ayu, serabi, pukis, bolu panggang, kue tambang, donat, dan bakwan. Makanan jajanan yang diteliti per 100 gram memiliki kadar air 11.2%-53.3%, kadar abu 0.60%-2.17%, kadar protein 3.72%-12.85%, kadar lemak 1.94%-29.22%, kadar karbohidrat 13.6%- 67.15%, serta energi 217 kkal–453 kkal. Kandungan zat besi bervariasi yaitu 3.55–10.29 mg/100 g dan kandungan seng 2.23–5.20 mg/100 g. Bioavailabilitas zat besi pada sampel berkisar antara 5.84%-25.58% dan seng berkisar antara 10.36%-36.48%. Bioavailabilitas zat besi dan seng ditemukan tertinggi pada putu ayu dan bolu panggang, sedangkan bioavailabilitas besi dan seng terendah dijumpai pada kue tambang. Apabila dikonsumsi dalam jumlah 100 g maka jumlah zat besi dan seng dalam makanan yang diteliti dapat menyumbang 3.55%- 10.29% AKG untuk zat besi dan 2.23%-4.51% AKG untuk seng. Semua makanan jajanan apabila dikonsumsi dengan jumlah 100 g, dapat memberikan kontribusi zat besi sebesar 20% AKG untuk pria dewasa sedangkan hanya kue tambang, donat, bakwan yang memenuhi 20% AKG untuk wanita dewasa. Sementara untuk seng kondisi tersebut diperoleh untuk konsumsi 100 g semua makanan jajanan memberikan kontribusi seng kurang dari 20% AKG untuk pria dewasa sedangkan bolu kukus, serabi, pukis, kue tambang, donat dan bakwan untuk wanita dewasa. Hasil yang diperoleh menunjukkan, serabi memberikan kontribusi zat besi paling tinggi, pukis memberikan kontribusi seng paling tinggi, sedangkan kue tambang memberikan kontribusi zat besi dan seng paling rendah terhadap 20% AKG untuk orang dewasa dibandingkan makanan jajanan lain.
Keyword: Bioavailabilitas, Kontribusi, Seng, JajSeng, Jajanan Tradisional, Zat Besianan Tradisional, Zat Besi

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