Judul: Identification Of Differentiating Indicators Of Formaldehyde Origin Based On Parameters Of Quality Deterioration Value In Tuna, Mackerel and Yellowtail.
Abstrak: Keamanan pangan merupakan suatu hal yang perlu diperhatikan dalam membangun sektor perikanan. Penyalahgunaan formalin sebagai bahan pengawet ikan merupakan salah satu permasalahan yang kerap ditemukan di Indonesia. Akan tetapi, secara alami ikan dapat membentuk senyawa formaldehida melalui reaksi trimetilamin oksida (TMAO). Hal ini menyebabkan sulitnya membedakan ikan dengan dan tanpa penambahan formaldehida. Penelitian ini bertujuan mengindentifikasi indikator pembeda berdasarkan parameter laju kemunduran mutu (organoleptik, nilai pH, TVB, FA, dan DMA) pada ikan tongkol, ekor kuning dan kembung dengan penambahan dan tanpa penambahan formalin selama 8 minggu dalam penyimpanan beku. Indikasi ikan yang mengandung formalin selama masa penyimpanan yaitu tingginya kandungan formaldehida serta DMA seiring dengan lama penyimpanan. Parameter organoleptik pada penelitian ini belum dapat dijadikan sebagai parameter pembeda yang kuat, The development of the fishing industry must take food safety into account. Formalin misused is one of the problems that is often found in Indonesia. However, fish are naturally able to form formaldehyde compounds through the reaction of trimethylamine oxide (TMAO). As a result, it becomes challenging to distinguish between fish possessing endogenous formaldehyde content and fish containing formalin during storage. This study aims to identify differentiating indicators with and without the addition of formaldehyde in tuna, mackerel, and yellowtail based on quality rating rate parameters (organoleptic, pH value, TVB, formaldehyde, and DMA) for 8 weeks with frozen storage. Indications of fish containing formalin during storage are high levels of total formaldehyde and DMA. Organoleptic parameters in this study could not be used as strong differentiating parameters.
Keyword: formaldehida, ikan, indikator, suhu beku

Judul: Identifikasi Indikator Pembeda Asal Formaldehida Berdasarkan Parameter Kemunduran Mutu Ikan Karang
Abstrak: Spesies ikan karang seperti baronang, kerapu dan kuniran dapat membentuk senyawa formaldehida alami selama proses penyimpanan. Oknum pedagang kerap kali menambahkan formaldehida artifisial untuk mengawetkan ikan, sehingga formaldehida alami dan artifisial sulit untuk dibedakan. Oleh karena itu, dibutuhkan parameter yang dapat membedakan formaldehida alami yang terbentuk serta formaldehida artifisial selama proses penyimpanan. Penelitian dilakukan selama 8 minggu penyimpanan beku dengan pengujian setiap dua minggu sehingga memperoleh lima titik uji. Analisis data dilakukan dengan uji univariat menggunakan ANOVA serta multivariat dengan uji linear diskriminsn. Pengujian organoleptik pada setiap sampel memiliki rentan 9 hingga 7 pada akhir penyimpanan sehingga penambahan formaldehida artifisial tidak berpengaruh terhadap atribut sensori. Penambahan formaldehida artifisial tidak berpengaruh signifikan terhadap pembentukan DMA dan nilai pH namun berpengaruh signifikan terhadap Formaldehida dan TVB yang merupakan kombinasi kuat yang dapat membedakan formaldehida artifisial dan formaldehida alami
Keyword: Formaldehida, Ikan karang, Kemunduran Mutu, TVB

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