Judul: Pengaruh Penggunaan Ikan Remang (Muraenasox talabon) dan Telur Itik terhadap Sifat Kimia dan Fisik Kerupuk Udang Cendana (Metapenaeus brevicornis)
Abstrak: Penelitian ini bertujuan untuk mengetahui sampai sejauh mana pengaruh penggunaan ikan remang (Muraenasox -talabon) dan telur itik terhadap sifat kimia dan sifat fisik kerupuk udang cendana (Metapenaeus brevicornis). Rancangan percobaan yang digunakan adalah Rancangan Acak Lengkap pola faktorial dengan ulangan dua kali. Perlakuan yang diterapkan meliputi jumlah telur (0, 1 dan 2 butir ) dan perbandingan berat daging udang dengan daging ikan (4 kg : 0 kg, 2 kg : 2 kg dan 1 kg : 3 kg). Pengamatan secara objektif dilakukan terhadap kadar air , kadar lemak, kadar protein dan kemekaran kerupuk udang. Sedangkan pengamatan secara organoleptik dilakukan terhadap warna, aroma, rasa, kemekaran dan kerenyahan kerupuk udang tersebut.
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Judul: Pengaruh penambahan daging ikan segar dan konsentrat protein ikan Remang (Congresox talabon) terhadap mutu kerupuk sagu
Abstrak: Penelitian mengenai pengaruh penambahan daging segar dan Konsentrat Protein Ikan (KPI) Remang telah dilakukan. Penelitian ini bertujuan untuk memanfaatkan ikan yang bernilai ekonomi rendah, serta mempelajari pengaruh penambahan daging segar dan KPI Remang terhadap mutu kerupuk sagu. Rancangan percobaan yang digunakan dalam penelitian ini adalah Rancangan Acak Lengkap (RAL) dengan 2 faktor. Faktor pertama adalah jenis suplemen (A) yang terdiri atas 2 taraf : ikan segar (A₁) dan KPI (A2), sedangkan faktor kedua adalah tingkat penambahan suplemen (B) yang terdiri atas 4 taraf 0% (Bo), 10% (B₁), 20% (B2), dan 30% (B3). Produk akhir dari masing-masing perlakuan kemudian dianalisa mutunya yang meliputi analisa kimia (proksimat), analisa fisik (kekerasan obyektif dan derajat pengembangan linear), dan uji organoleptik (kenampakan, warna, kerenyahan, aroma, dan rasa). Kadar protein kerupuk meningkat seiring dengan peningkatan penambahan suplemen. Kenaikan kadar protein ini linear dengan persamaan regresi Ý (untuk ikan segar) dan Ŷ = = 0,348 + 0,117X 0,922 + 0,647X (untuk KPI). Parameter kimia lain yang mengalami kenaikan seiring dengan peningkatan penambahan suplemen adalah kadar air, dan kadar abu. Kenaikan kadar air dipengaruhi secara nyata oleh kedua faktor, kenaikan kadar abu hanya dipengaruhi oleh tingkat penambahan suplemen. Kadar lemak, kadar serat kasar, kadar karbohidrat, dan nilai kalori mengalami penurunan. Penurunan nilai parameter- parameter tersebut dipengaruhi secara nyata oleh kedua faktor. Derajat pengembangan linear kerupuk menunjukkan penurunan secara nyata, seiring dengan peningkatan penambahan suplemen. Sedangkan parameter fisik yang lain yakni nilai kekerasan obyektif mengalami kenaikan, akan tetapi kenaikan ini tidak dipengaruhi secara nyata oleh kedua faktor…dst
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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