Judul: Karakteristik karton dengan bahan dasar eceng gondok dan kertas koran bekas menggunakan perekat alami kitosan
Abstrak: Eceng gondok merupakan bahan berserat lignoselulosa yang potensial sebagai bahan baku pembuatan karton. Namun, karton yang dihasilkan dari bahan dasar eceng gondok masih berkualitas rendah. Di sisi lain, kertas koran bekas merupakan limbah yang menngandung serat dan belum termanfaatkan secara optimal.
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Judul: Pemanfaatan kertas koran bekas sebagai bahan baku dalam pembuatan kertas karton
Abstrak: Meningkatnya konsumsi domestik pulp dan kertas dan makin terbatasnya sumber bahan baku konvensiaonal (kayu) menimbulkan pemikiran pencarian bahan baku alternatif. Salah satu usaha yang dapat dipertimbangkan adalah pemanfaatan kembali atau pendaur ulangan kertas koran bekas sebagai bahan baku. Disamping kertas koran bekas, sumber lain yang dapat dipertimbangkan adalah limbah padat berkadar air tinggi dari industri pulp dan kertas (sludge) yang selama ini hanya dibuang sebagai bahan pencampur. Campuran kertas koran bekas dan sludge antara lain dapat digunakan untuk pembuatan kertas karton. Penelitian ini bertujuan untuk mengetahui kualitas kertas karton dari kertas koran bekas dan sludge pada berbagai kombinasi campuran. Perlakuan yang diberikan adalah pencampuran pulp daur ulang kertas koran dengan sludge pada perbandingan 100:0; 90:10; 80:20; 70:30 (dalam persen berdasarkan berat kering tanur), dengan tiga ulangan. Lembaran dibuat dengan gramatur target 125gr/m². Parameter yang dianalisa adalah sifat fisik dan optik lembaran yang meliputi dimensi serat, rendemen, indeks sobek, indeks tarik, kekakuan, nilai ring crush, nilai concora, derajat putih dan opasitas. Rendemen serat yang terdapat di dalam sludge adalah 31,20 persen dan rendemen daur ulang kertas koran 84,36 persen. Panjang serat sludge berkisar 181,25μm-3000µm dengan rata-rata 862,78µm dan didominasi oleh serat terpotong atau tidak utuh. Panjang serat pulp koran bekas berkisar 300µm- 1305µm dengan rata-rata 852,50µm dan tebal dinding sel rata-rata 4,21µm dengan lumen rata-rata 8,65µm. ...
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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