Judul: Aktivitas Antioksidan dan Mutu Sensori Minuman Fungsional Mikroenkapsulasi Berbasis Ekstrak Daun Sirih Merah
Abstrak: Minuman fungsional berbasis ekstrak daun sirih merah dengan kombinasi berbagai ekstrak rempah diketahui mengandung aktivitas antioksidan namun hasil uji sensori minuman tersebut memiliki rasa pahit. Penelitian ini bertujuan mengembangkan minuman fungsional berbasis ekstrak daun sirih merah menggunakan teknik mikroenkapsulasi dengan membandingkan kadar fenol, aktivitas antioksidan serta mutu sensori dengan minuman fungsional ready to drink (RTD). Pembuatan minuman fungsional mikroenkapsulasi dilakukan dengan menggunakan penyalut maltodekstrin konsentrasi 10 dan 20 %. Aktivitas antioksidan diuji dengan metode CUPRAC. Minuman fungsional ready to drink (RTD) memiliki kandungan total fenolik dan aktivitas antioksidan sebesar 782.30±2.54 mg GAE/g dan 1660.19±31.67 μmol Tr/g lebih tinggi dibandingkan minuman fungsional mikroenkapsulasi. Minuman fungsional mikroenkapsulasi dengan penyalut maltodekstrin 10% merupakan formulasi terpilih karena memiliki ukuran partikelnya yang paling kecil (1.283 μm), kandungan total fenolik (12.90±0.01 mg GAE/g) dan aktivitas antioksidan (189.41±1.88 μmol Tr/g) yang lebih tinggi. Minuman fungsional mikroenkapsulasi memberikan mutu sensori (2.30±0.75) yang lebih disukai dibandingkan minuman ready to drink (RTD).
Keyword: mikroenkapsulasi, antioksidan, sirih merah, minuman fungsional

Judul: Aktivitas Antioksidan dan Total Fenolik Minuman Fungsional Berbasis Ekstrak Sirih Merah
Abstrak: Pengembangan minuman fungsional dari ekstrak sirih merah yang ditambahkan dengan beberapa ekstrak rempah telah dilakukan sebelumnya. Akan tetapi, hasil uji sensori masih tergolong rendah karena masih terdapat rasa pahit pada minuman fungsional tersebut. Penelitian ini bertujuan mengembangkan minuman fungsional berbasis ekstrak daun sirih merah dengan teknologi nanoenkapsulasi dan mengevaluasi total fenolik, aktivitas antioksidan, serta mutu sensori yang dibandingkan dengan minuman ready to drink (RTD). Pembuatan minuman fungsional nanoenkapsulasi menggunakan wall materials berupa gum arab dan maltodekstrin (1:3; 1:1; 3:1), sedangkan core materialsnya berupa campuran ekstrak daun sirih merah dan rempah-rempah (kayu manis, jahe merah, jeruk nipis). Aktivitas antioksidan menggunakan metode CUPRAC. Hasil penelitian menunjukkan bahwa minuman ready to drink memiliki nilai total fenol (786.23 ± 1.52 mg GAE/g) dan aktivitas antioksidan (1583.97 ± 85.52 μM troloks/g ekstrak) lebih baik dibandingkan dengan minuman fungsional nanoenkapsulasi. Akan tetapi, minuman fungsional nanoenkapsulasi memberikan mutu sensori yang lebih disukai dibandingkan dengan minuman ready to drink.
Keyword: Minuman fungsional, sirih merah, nanoenkapsulasi

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