Judul: Risk Assessment of Commercial Smoked Shark Consumption
Abstrak: Merkuri merupakan jenis logam berat yang dapat memberikan dampak negatif bagi tubuh. Bentuk merkuri yang paling toksik adalah metil merkuri. Ikan hiu memiliki kemungkinan mengandung merkuri yang tinggi akibat biomagnifikasi. Indonesia merupakan salah satu pemasok utama hiu di dunia. Bentuk olahan ikan hiu adalah hiu asap. Pengasapan dapat menghasilkan senyawa polisiklik aromatik hidrokarbon (PAH) yang diantaranya bersifat karsinogen. Penelitian bertujuan menentukan tingkat cemaran merkuri, metil merkuri, PAH, dan menghitung analisis risiko konsumsi hiu asap. Metode penelitian meliputi uji merkuri, metil merkuri dan kandungan PAH pada ikan hiu asap dan penyebaran kuesioner. Seluruh sampel mengandung merkuri dengan 4 sampel yang melebihi ambang batas (1 ppm). Kandungan metil merkuri terdeteksi pada 6 sampel dengan 3 sampel yang melebihi batas (1 ppm). Kandungan PAH pada sampel terdeteksi dengan jumlah yang tidak melebihi batas maksimum. Hasil analisis risiko menunjukkan nilai Hazard Index (HI) >1 yang berarti risiko dari mengonsumsi hiu asap adalah signifikan dan kronis., Mercury is a type of heavy metal that can have a negative impact on the body. The most toxic form of mercury is methylmercury. Sharks are likely to contain high levels of mercury due to biomagnification. Indonesia is one of the main suppliers of shark in the world. The processed form of shark is smoked shark. Smoking can produce polycyclic aromatic hydrocarbon (PAH) compounds, some of which are carcinogens. The study aims to determine the level of mercury, methyl mercury, PAH, and calculate the risk analysis of smoked shark consumption. Research methods included testing mercury, methylmercury and PAH content in smoked shark and distributing questionnaires. All samples contained mercury with 4 samples exceeding the threshold (1 ppm). Methylmercury was detected in 6 samples with 3 samples exceeding the limit (1 ppm). PAH content in the samples was detected with an amount that did not exceed the maximum limit. The results of the risk analysis showed a Hazard Index (HI) value >1, which means that the risk from consuming smoked shark is significant and chronic.
Keyword: heavy metal, hazard index, chronic

Judul: Detemination Heavy Metal Contamination and Species Identification in Shark Products Using DNA Barcoding
Abstrak: Hiu merupakan konsumen tingkat tinggi (apex predator) yang dapat mengakumulasi logam berat melalui sumber makanannya. Hiu olahan yang mengandung logam berat dalam jumlah tinggi dapat membahayakan manusia. Hiu olahan dijual dalam bentuk potongan kecil dan sulit diidentifikasi spesiesnya, sehingga memerlukan metode molekuler berbasis DNA. Penelitian bertujuan menentukan cemaran logam berat pada olahan hiu, mengidentifikasi spesies menggunakan metode DNA barcoding serta menentukan korelasi antar faktor dalam produksi ikan hiu asap di 3 wilayah Pantura. Metode penelitian ini meliputi uji logam berat, isolasi DNA, amplifikasi DNA dengan marka gen COI, sekuensing, dan penyebaran kuesioner. Spesies yang teridentifikasi meliputi C. sealei, C. tjutjot, C. limbatus, S. lewini, dan R. springeri. Kelima sampel mengandung logam berat merkuri terutama spesies C. limbatus dan S. lewini yang jumlahnya melebihi 1 mg/kg. Hiu olahan masih banyak diminati masyarakat Indonesia khususnya wilayah Pantura dengan volume penjualan rata rata lebih dari 1 kuintal per minggu. Pendidikan dan pendapatan produsen berkorelasi positif dengan volume penjualan hiu di wilayah Pantura., Sharks are high-level consumers (apex predators) that can accumulate heavy metals through their food sources. Processed sharks containing high amounts of heavy metals can harm humans. Processed sharks are sold in the market in the form of small pieces, making it difficult to identify the species, requiring DNA-based molecular methods. The aims of the study were to determine heavy metal contamination in processed sharks, to identify species using the DNA barcoding method and to determine the correlation between factors in smoked shark production in 3 regions of Pantura. The research methods included DNA isolation, DNA amplification with COI gene markers, electrophoresis, sequencing, heavy metal testing, and distributing questionnaires. The identified species include C. sealei, C. tjutjot, C. limbatus, S. lewini, and R. springeri. The five samples contained the heavy metal mercury, especially the species C. limbatus and S. lewini whose amount exceeded 1 mg/kg. Processed sharks are still in great demand by the people of Indonesia, especially in Pantura, with an average sales volume of more than 1 quintal per week. The sales volume of sharks is positively correlated with the education and income of producers in the Pantura.
Keyword: amplifikasi, DNA barcoding, logam berat, amplification, heavy metals

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