Judul: Chemical Composition and Identification of Antioxidant Compounds from the Extracts of Tambelo (Bactronophorus thoracites)
Abstrak: Tambelo (Teredinidae) is consumed freshly by local people in Papua without removing the digestive tracts. This study aimed to investigate the chemical composition of fresh tambelo, to conduct antioxidant and phytochemistry tests, and to identify the compound of active antioxidant obtained from selected fractionated extract. The research consisted of two stages. The first stage involved the analyses of proximate, fatty acid, amino acid and mineral. The second stage was the extraction of active materials by means of maceration. The maceration result was evaporated and partitioned with a solvent of n-hexane and ethyl acetate. Next, the extract underwent phytochemical and antioxidant tests (20, 40. 60, and 80 ppm) with DPPH method applying BHT and vitamin super ester C as the standard with a concentration of 4, 6, 8 and 10 ppm. Finally, the identification of fresh antioxidant compound was carried out using LC-MS. The composition of fresh tambelo divided into moisture 82,72±0,01%, protein 7,21±0,31%, fat 0,28±0,04%, ash 2,07±0,27%, and carbohydrate (by difference) 7,72±0,62%. The composition of dried tambelo was moisture 6,63±0,01%, protein 42,77±2,01%, fat 14,27±0,22%, ash 5,88±1,04%, carbohydrate (by difference) 30,45±2,83%. The protein of tambelo contained nine essential amino acids and eight non essensial amino acids. It had seven saturated fatty acids and eight unsaturated fatty acids. It contained calcium of 3532,46 ppm and phosfor of 2363,06 ppm. The highest antioxidant activity was found in the crude extract of ethyl acetate with IC50 of 15 ppm. The highest antioxidant activity obtained by column fractionation was the 9th fraction with IC50 of 8.87 ppm. Phytochemical test, described the crude extract of tambelo contained a group of alkaloid, flavonoid, steroid and triterpenoid. MarinLit database (Blunt and Blunt 2008), 9th fraction was similar to farnesic acid glyceride, sesquiterpenoic acid glyceride and labdane diterpenoid.
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Judul: Komposisi Kimia Tambelo (Bactronophorus Sp.) Dan Karakteristik Produk Hasil Fermentasinya.
Abstrak: Tambelo (Bactronophorus sp.) adalah hewan penggerek kayu yang dikelompokkan kedalam filum moluska. Tambelo hidup pada batang kayu bakau yang telah mati dan mengalami proses pembusukan. Pengalaman empiris masyarakat pantai Sulawesi Tenggara dan beberapa hasil penelitian menunjukkan tambelo memiliki nilai gizi yang tinggi sehingga bermanfaat bagi kesehatan. Tambelo jenis Bactronophorus sp. di pantai Sulawesi Tenggara ditemukan hidup pada bakau Rhizophora sp., Bruguiera sp. dan Sonneratia sp. Tambelo mudah membusuk dan pengolahan tambelo belum banyak dilakukan sehingga tingkat pemanfaatannya rendah. Oleh karena itu pembuatan tambelo fermentasi dapat menjadi alternatif pemanfaatan hewan ini. Fermentasi secara tidak spontan memberikan kualitas fisika-kimia dan mikrobiologi produk lebih baik dibanding fermentasi spontan dan bakasang berpotensi sebagai starter untuk membuat produk fermentasi. Tujuan penelitian ini adalah menentukan profil berat dapat dimakan (BDD), komposisi kimia, asam amino, asam lemak dan kelompok senyawa kimia tambelo yang hidup pada bakau Rhizophora sp., Sonneratia sp. dan Bruguiera sp. di perairan pantai Sulawesi Tenggara, membuat produk tambelo fermentasi dan menentukan profil komposisi kimia dan asam amino tambelo fermentasi. Penelitian ini dilakukan pada bulan Januari 2014 hingga Agustus 2014. Penelitian dilaksanakan dalam tiga tahap. Tahap pertama adalah karakterisasi daging tambelo yang meliputi analisis bagian dapat dimakan (BDD), komposisi kimia, asam amino, asam lemak, dan kelompok senyawa kimia. Tahap kedua adalah pembuatan produk fermentasi tambelo. Nilai pH, kadar NaCl, total mikroba dan total bakteri asam laktat (BAL) dianalisis setiap minggu hingga 4 minggu fermentasi. Tahap ketiga adalah karakterisasi produk tambelo fermentasi yang meliputi analisis komposisi kimia dan asam amino. Tambelo yang hidup pada bakau Rhizophora sp. memiliki kadar protein dan kadar lemak lebih tinggi dibanding tambelo pada Sonneratia sp. dan Bruguiera sp. Selama fermentasi nilai pH menurun dan kadar NaCl cenderung stabil. Total mikroba dan bakteri asam laktat (BAL) meningkat sampai minggu kedua, dari minggu kedua sampai minggu ketiga cenderung stabil, kemudian dari minggu ketiga sampai minggu keempat mengalami penurunan. Sebelum fermentasi tambelo megandung kadar air 82,51 % dan setelah fermentasi 82 %. Kadar abu sebelum fermentasi 2,27 % dan setelah fermentasi 1,48 %. Kadar protein sebelum fermentasi 8,21 % dan setelah fermentasi 9,50 %. Kadar lemak sebelum fermentasi 3,34 % dan setelah fermentasi 0,42 %. Kadar karbohidrat (by different) sebelum fermentasi 3,67 % dan setelah fermentasi 6,60 %. Persentasi asam amino bebas pada tambelo fermentasi cenderung meningkat dibandingkan sebelum fermentasi. Mikroba yang ada pada bakasang mampu menghidrolisis komponen yang ada pada tambelo sehingga bakasang dapat digunakan sebagai starter untuk fermentasi tambelo.
Keyword: bakasang, fermentasi, tambelo

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