Judul: Formulasi Teh Daun Sirih Merah (Piper Crocatum) Sebagai Minuman Fungsional
Abstrak: Piper crocatum yang lebih dikenal dengan sirih merah banyak digunakan sebagai tanaman hias yang dapat memberikan dampak positif bagi kesehatan. Penelitian ini dilakukan untuk membuat teh dari daun sirih merah sebagai minuman fungsional. Terdapat tiga formula teh daun sirih merah yaitu 50%, 75%, dan 100%. Berdasarkan hasil uji organoleptik, teh daun sirih merah belum dapat diterima dari segi rasa karena memiliki rasa pahit yang sangat kuat, sedangkan aroma dan warna dari teh daun sirih merah dapat diterima. Hasil penelitian menunjukkan bahwa kandungan air teh daun sirih merah kering berada pada kisaran 4.00-5.75% dan kadar ekstrak dalam air teh daun sirih merah berkisar antara 10.10-11.12%. Ketiga formula tersebut memiliki kadar abu total sebesar 11.16-15.29%, abu tak larut air sebesar 18.78-21.89%, abu larut air sebesar 78.11-81.22%, dan alkalinitas abu (KOH) sebesar 0.70-0.99%. Kadar polifenol pada ketiga formula teh daun sirih berkisar antara 4.67-14.01% dan tidak terdapat cemaran logam timbal pada ketiga formula tersebut. Aktivitas antioksidan pada ketiga formula tersebut berkisar antara 1.15-11.49 mg Vit C/sajian. Kapasitas antioksidan tertinggi dimiliki oleh formula 50% daun sirih merah kering dan 50% daun teh (Camellia sinensis).
Keyword: antioksidan, minuman fungsional, sirih merah, teh

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: 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