Judul: Karakterisasi dan Aktivitas Penghambatan Enzim Tirosinase oleh Kolagen dari Teripang
Abstrak: Kolagen adalah protein fibrosa dan komponen utama jaringan ikat yang dijumpai di tulang, tendon, kulit, pembuluh darah, dan kornea mata. Salah satu biota laut yang dapat dijadikan sumber kolagen alternatif adalah teripang. Paparan Sinar UV dalam jangka waktu yang lama dapat menyebabkan gangguan pada kulit serta meningkatkan sintesis melanin di kulit dan menyebabkan hiperpigmentasi oleh enzim tirosinase. Penelitian ini bertujuan mengisolasi, melakukan karakterisasi, dan menentukan aktivitas penghambatan enzim tirosinase oleh kolagen teripang. Isolasi kolagen terdiri atas praperlakuan dan hidrolisis dengan asam. Praperlakuan menggunakan NaOH 0.1 M dan hidrolisis dengan asam asetat 0.5 M selama 48 jam. Hasil praperlakuan jam ke-48 menunjukkan kadar protein non kolagen 0.090 mg/mL. Kolagen teripang emas memiliki rendemen 0.66%, nilai pH 6.91, gugus fungsi khas amida A (3379.29 cm-1), amida B (2924.09 cm-1), amida I (1681,93 cm- 1), amida II (1568.13 cm-1), dan amida III (1269.16 cm-1). Aktivitas penghambatan tirosinase menunjukkan bahwa kolagen teripang emas memiliki nilai IC50 5610 ppm.
Keyword: enzim tirosinase, kolagen, kolagen larut asam, teripang

Judul: Karakteristik dan Aktivitas Antioksidan Kolagen Larut Asam dari Teripang Emas (Stichopus hermanii).
Abstrak: Kolagen adalah protein utama penyusun jaringan ikat yang ditemukan di tulang, tendon, kulit, pembuluh darah, dan kornea mata. Kolagen dalam tubuh manusia berkurang seiring bertambahnya usia. Teripang emas merupakan salah satu biota laut yang berpotensi sebagai sumber kolagen. Teripang emas juga banyak mengandung senyawa bioaktif dan memiliki efek farmakologi salah satunya sebagai antioksidan. Penelitian ini bertujuan mengisolasi kolagen dengan metode kolagen larut asam, menentukan aktivitas antioksidan, dan melihat gugus fungsi dengan FTIR teripang jenis Stichopus hermanii. Isolasi kolagen terdiri atas praperlakuan dan hidrolisis dengan asam. Praperlakuan menggunakan NaOH 0.1 M dan hidrolisis dengan asam asetat 0.5 M selama 48 jam. Hasil praperlakuan menunjukkan kadar protein non kolagen 0.052 mg/mL dan nilai pH 6.90. Kolagen teripang emas memiliki rendemen 0.7145%, gugus fungsi khas amida A (3379.29 cm- 1), amida B (2924.09 cm-1), amida I (1681,93 cm-1), amida II (1568.13 cm-1), dan amida III (1269.16 cm-1). Aktivitas antioksidan menunjukkan bahwa kolagen teripang emas memiliki nilai IC50 5.84 ppm.
Keyword: kolagen, kolagen larut asam, teripang emas, Antioksidan

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