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: Identification of Distinguishing Indicators of Natural Formaldehyde Based on Parameters of Quality Deterioration Value in Freshwater Fish
Abstrak: Ikan nila, mas, dan patin merupakan ikan air tawar komersil dan dagingnya menjadi komoditas ekspor dalam bentuk segar atau dingin dan beku. Formalin menjadi salah satu bahan berbahaya yang penggunaannya sering disalahgunakan sebagai pengawet pada ikan untuk mencegah kerusakan ikan. Formaldehida dapat terbentuk secara alami pada ikan selama proses kemunduran mutu ikan melalui reaksi reduksi trimetilamin oksida (TMAO) yang terjadi secara enzimatik dengan hasil samping berupa dimetilamin (DMA). Penelitian ini bertujuan menentukan indikator pembeda dengan penambahan formalin dan tanpa penambahan formalin pada ikan nila, mas, dan patin selama 8 minggu dengan penyimpanan beku. Parameter yang diamati adalah morfometrik, organoleptik, pH, total volatile base (TVB), formaldehida (FA), dan dimetilamin (DMA). Parameter FA dan TVB dapat dijadikan sebagai indikator pembeda antara kedua perlakuan tersebut. Parameter mata juga dapat dijadikan sebagai indikator pembeda, akan tetapi bukan merupakan parameter yang kuat., Tilapia, carp, and pangas catfish are commercial freshwater fish and their meat is an export commodity in fresh or chilled and frozen form. Formalin is one of the hazardous materials whose use is often misused as a preservative in fish to prevent fish damage. Formaldehyde can be formed naturally in fish during the process of deterioration of fish quality through the reduction reaction of trimethylamine oxide (TMAO) which occurs enzymatically with by-products in the form of dimethylamine (DMA). This study aims to determine the differentiating indicators with and without the addition of formalin in tilapia, carp, and catfish for 8 weeks with frozen storage. Parameters observed were morphometric, organoleptic, pH, total volatile base (TVB), formaldehyde (FA), and dimethylamine (DMA). The the parameters FA and TVB can be used as indicators of the difference between the two treatments. The eye parameter can also be used as a indicator, but it is not a strong parameter.
Keyword: carp, natural formaldehyde, nile tilapia, pangas catfish, quality deterioration

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