Judul: Determination of Total Phenolics and Flavonoids Content of Ethyl Acetate and Water of Red Betel Extract (Piper crocatum Ruiz & Pav.) and Their Inhibition of Tyrosinase Enzyme
Abstrak: Sirih merah (Piper crocatum Ruiz & Pav.) memiliki senyawa potensial seperti flavonoid yang dapat berperan sebagai inhibitor tirosinase. Penelitian ini bertujuan untuk menentukan kadar total fenolik dan flavonoid ekstrak sirih merah berdasarkan perbedaan pelarut serta aktivitas inhibisinya terhadap enzim tirosinase. Ekstrak yang mampu menginhibisi enzim tirosinase mampu bekerja sebagai agen pemutih, yang bisa digunakan dalam suatu krim pemutih. Metode ekstraksi yang digunakan yaitu maserasi untuk pelarut etil asetat dan perebusan untuk pelarut air, dilanjutkan dengan uji kadar total fenolik, flavonoid, dan uji inhibisi tirosinase yang berpotensi sebagai agen pemutih. Hasil yang diperoleh pada penelitian ini menunjukkan ekstrak etil asetat menjadi sampel terbaik dengan nilai total fenolik sebesar 56,255 mg GAE/g DW, kadar total flavonoid sebesar 48,631 mg QE/g DW, dan berpotensi sebagai penghambat tirosinase dengan nilai IC50 1600,38 mg/L. Sementara ekstrak air memiliki nilai kadar total fenolik sebesar 44,2 mg GAE/g DW, kadar total flavonoid sebesar 39,812 mg QE/g DW, dan berpotensi sebagai penghambat tirosinase dengan nilai IC50 1764,39 mg/L., Red betel (Piper crocatum Ruiz & Pav.) is a plant with flavonoid-like potential compound that can act as tyrosinase inhibitor. This research aimed to determine the total phenolic and flavonoid concentration of red betel extract based on different solvents and its inhibition activities against the tyrosinase enzyme. Extract capable of inhibiting the tyrosinase enzyme had potential to act as a whitening agent, which could then be utilized in a whitening cream product. The extraction methods include: maceration for the ethyl acetate solvent; and boiling for the water solvent, continued with measuring the phenolic and flavonoid content, with the tyrosinase inhibition test for solvents with whitening agent potential. Results indicated that the ethyl acetate solvent was the best sample with a total phenolic value of 56,255 mg GAE/g DW, total flavonoid content of 48,631 mg QE/g DW, and the potential to inhibit tyrosinase with an IC50 value of 1600,38 mg/L. Meanwhile, the water extract resulted in a total phenolic value of 44,2 mg GAE/g DW and a total flavonoid content of 39,812 mg QE/g DW, with the potential to inhibit tyrosinase with an IC50 value of 1764,39 mg/L.
Keyword: flavonoid, phenolic, red betel, solvent, tyrosinase

Judul: Total Phenolics, Flavonoids, and ɑ-Glucosidase Inhibitory Activity of n-Hexane, Ethyl Acetate and Methanol Extracts of Red Betel (Piper crocatum)
Abstrak: Sirih merah (Piper crocatum) mempunyai berbagai senyawa dengan kemampuan penghambatan enzim ɑ-glukosidase. Penelitian ini bertujuan menentukan ekstrak yang memiliki tingkat kandungan fenolik dan flavonoid yang optimal, serta penghambatan tertinggi pada enzim α-glukosidase. Metode maserasi bertingkat digunakan untuk mengekstraksi senyawa bioaktif sirih merah menggunakan pelarut-pelarut dengan peningkatan kepolaran (n-heksana, etil asetat, metanol). Pengujian dilakukan terhadap kadar fenolik total, flavonoid, serta kemampuan inhibisi ɑ-glukosidase. Hasil pengukuran total kadar fenolik, flavonoid, dan aktivitas penghambatan α-glukosidase menunjukkan nilai yang secara signifikan berbeda (p<0,05) pada semua ekstrak dengan total kadar fenolik dan flavonoid tertinggi pada ekstrak n-heksana berturut-turut sebesar 25,68 ± 0,29 mg GAE/g dan 65,84 ± 0,96 mg QE/g. Sementara itu, ekstrak metanol daun sirih merah memiliki daya hambat diantara semua ekstrak dengan nilai persen inhibisi α-glukosidase sebesar 43,87 ± 1,83%., Red betel leaf (Piper crocatum) had various compounds with the ability to inhibit α-glucosidase enzymes. The research aimed to determine the extract with optimal levels of phenolic and flavonoid content, as well as the highest inhibition of α-glucosidase enzyme. A multistage maceration method was used to extract bioactive compounds from red betel leaf using solvents with increasing polarity (n-hexane, ethyl acetate, methanol). Tests were conducted on the total phenolic content, flavonoids, and α-glucosidase inhibition ability. The measurement results of the total phenolic content, flavonoids, and α-glucosidase inhibition activity showed significantly different values (p<0,05) in all extracts with the highest total phenolic and flavonoid content in n-hexane extract, respectively, at 25,68 ± 0,29 mg GAE/g and 65,84 ± 0,96 mg QE/g. Meanwhile, the methanol extract of red betel leaf had inhibitory power among all extracts with a percentage inhibition of α-glucosidase at 43,87 ± 1,83%.
Keyword: ɑ-glukosidase/ɑ-glucosidase, antidiabetes/antidiabetic, fenolik/phenolic, flavonoid/flavonoid, sirih merah/red betel

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