Judul: Hubungan komposisi bahan dengan sifat termal buah-buahan
Abstrak: Difusivitas panas buah manggis juga ditentukan secara tidak langsung dengan mengetahui lebih dulu nilai- nilai konduktivitas panas, massa jenis dan panas ienis buah tersebut. Penentuan nilai konduktivitas panas masing-masing komponen diduga menggunakan metoda tahanan listrik atau dengan menggunakan model yang telah dikembangkan yaitu Model Series dan Model Paralel.. + Peralatan untuk pengukuran difusivitas panas telah berhasil dirangkai mempunyai hasil keluaran berupa aliran udara dingin dengan suhu mencapai 10 °C dengan kecepatan udara 1,96 m/s. Mesin pendingin tipe terowongan angin terdiri dari mesin kompresi dan kondensor tipe T-50 buatan Jepang, blower, evaporator, dan terowongan angin segi empat berpenampang 0.3 x 0.3 meter persegi. Mesin ini memerlukan tegangan listrik 350 volt dan daya listrik 2.753 kilowatt. Sampel manggis 5 buah dengan ukuran diameter 7 cm dan standart deviasi bobot 130.44 ± 18.33 gram didapat nilai a rata-rata dengan metoda numerik yaitu 0.634 ± 0.131 x 10-7 m²/detik. Dengan metoda curve fitting didapat nilal α rata-rata sebesar 0.364 X 10-7 m²/detik. Perhitungan...
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Judul: Penentuan Nilai Difusifitas Panas Mangga ( Mangifera indica L ) untuk Simulasi Pendinginan pada Mesin Pendinginan Jet Uap
Abstrak: Dalam pendinginan, penggunaan energi dapat dihemat dengan mengetahui lama proses yang diperlukan untuk menurunkan suhu bahan yang disimpan, dari suhu kamar menjadi suhu simpan yang optimum. Lama proses dapat diketahui dengan suatu simulasi pendinginan dengan difusivitas panas bahan sebagai parameter masukan. Penelitian ini bertujuan untuk mempelajari untuk mempelajari karakteristik kotak penyimpan dingin pada mesin pendingin jet uap dengan .~L enaga surya, dan membuat simulasi pendugaan lama proses pendinginan mangga (Mangifera indica, L) berdasarkan nilai difusivitas hasil pengukuran, sebagai parameter untuk menentukan desain kotak pendingin. Dalam ha1 ini dipilih mangga dari varitas Arummanis dan Cengkir.
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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