Judul: Pengaruh Penambahan Konsentrat Protein Ikan terhadap Mutu Mie Kering yang Dihasilkan
Abstrak: Sebagian besar masyarakat Indonesia masih tergantung dari beras atau nasi yang berfungsi utama sebagai sumber energi dan sumber protein. Salah satu alternatif dalam menggantikan makanan pokok tersebut adalah mie, yang diharapkan tidak mengurangi zat gizi yang digantikannya. Di lain pihak tidak bisa dipungkiri produk mie semakin populer dan banyak dikonsumsi, baik sebagai makanan utama maupun makanan selingan. Akan tetapi jenis makanan ini kurang didukung oleh nilai gizi (khususnya protein) yang tinggi. Sehingga timbul pernikiran untuk meningkatkan nilai gizi mie dengan jalan melakukan penambahan KPI.
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Judul: Pengaruh Suhu dan pelarut dalam pembuatan Konsentrat Protein Ikan dari Telur Ikan Tuna
Abstrak: Potensi perikanan di Indonesia sangat besar, di mana tuna merupakan salah satu kornoditi ekspor yang penting. Dari limbah pengalengan ikan tuna dihasilkan beberapa jenis limbah yang belum banyak dimanfaatkan, antara lain telur ikan yang jumlahnya mencapai 0.79 ton per hari pada PT Blarnbangan Raya, yang dapat diliianfaatkan lnenjadi konsentrat protein sebagai salah satu sumber protein bagi penduduk Indonesia. Penelitian dilakukan pada bulan Mei salnpai bulan Agustus dengan tujuari u n t i ~ k mempelajari kemungkinan pemanfaata~i relur ikan tuna ~nenjadi konsentrat protein ikan (KPI), serta mempelajari pengaruli suhu dan bahan pelarui terhadap rncltcl, sifhi fungsional dan rendemen KPI yang dihasilkan. Konsentrar proiein ikari (KPI) dibuat dengar1 inelode lama yang dimodifikasi dari metode US National Marine Fisheries Service. da~im etode bar11 yang dimodifikasi dari Metode Suzuki. detigan dua riiacanl cara pengadukan pada penelitian pendahuluan. Hasil yang terbaik adalal~ KPI yang dibuat dengan inetode baru yang diaduk dengan stirer pada kecepatan 2000 rpln.
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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