Judul: Total Flavonoids and Inhibitory Activity of Tamarind  Water Extract and Putri Malu against MAO-A as Antidepressants
Abstrak: Depresi merupakan gangguan mental yang sering tidak disadari dan disepelekan. Penanganan depresi diketahui membutuhkan biaya yang tidak sedikit. Alternatif lain untuk menekan biaya agar lebih terjangkau adalah dengan memanfaatkan bahan alam. Kandungan flavonoid pada tanaman asam jawa dan putri malu memiliki aktivitas antidepresan. Penelitian bertujuan menentukan kadar flavonoid total serta potensi ekstrak air dari tanaman asam jawa (Tamarindus indica L.) dan putri malu (Mimosa pudica L.) sebagai antidepresan melalui pengujian daya inhibisi terhadap aktivitas enzim MAO-A secara in vitro. Penentuan kadar flavonoid total menggunakan metode kolorimetri AlCl3 dengan standar kuersetin dan pengujian daya inhibisi MAO-A dilakukan secara in vitro. Kadar flavonoid total tertinggi sebesar 7,3150 mg QE/g ekstrak yang ditunjukkan dari bagian batang putri malu dan daya inhibisi tertinggi sebesar 87% oleh kulit batang asam jawa. Asam jawa dan putri malu memiliki nilai kadar flavonoid total tinggi dan terbukti bertindak sebagai antidepresan dengan menghambat aktivitas enzim MAO-A.
Keyword: antidepressants, flavonoids, MAO-A, Mimosa pudica, Tamarindusindica

Judul: Total Flavonoids and Inhibition of Secang and Kacang Gude Water Extract against the Activity Monoamine Oxidase A as an Antidepressant
Abstrak: Depresi adalah gangguan yang dapat memengaruhi perubahan mental yang disebabkan oleh peningkatan aktivitas enzim monoamina oksidase A (MAO-A). Pengujian terhadap aktivitas antidepresan dapat dilakukan secara in vitro dengan metode inhibisi terhadap enzim monoamina oksidase A (MAO-A) menggunakan ekstrak secang (Caesalpinia sappan L.) dan kacang gude (Cajanus cajan L.). Penelitian ini bertujuan menentukan total flavonoid serta aktivitas antidepresan ekstrak air secang dan kacang gude melalui inhibisi enzim MAO-A. Rendemen tertinggi diperoleh pada ekstrak air daun secang sebesar 23,72%. Hasil total flavonoid tertinggi dihasilkan oleh ekstrak air daun secang sebesar 10,69 mg KE/g ekstrak dan daun kacang gude sebesar 5,74 mg KE/g ekstrak. Pengujian daya inhibisi terhadap MAO-A dibandingkan dengan clorgyline sebagai inhibitor control (IC). Daya inhibisi terhadap MAO-A tertinggi dihasilkan oleh ekstrak air daun secang sebesar 87,08% dan biji kacang gude sebesar 76,06%. Nilai persentase inhibisi tersebut lebih besar dibandingkan clorgyline sebesar 67,59%. Hal ini menunjukkan bahwa ekstrak air daun secang dan biji kacang gude memiliki aktivitas sebagai agen antidepresan secara in vitro., Depression is a disorder that can affect mental changes caused by increased activity of the monoamine oxidase-A enzyme (MAO-A). Testing of antidepressant activity can be done in vitro by inhibition method against monoamine oxidase-A (MAO-A) enzyme using secang extract (Caesalpinia sappan L.) and kacang gude (Cajanus cajan L.). This study aimed to determine the total flavonoids and antidepressant activity of secang and kacang gude water extract through MAO-A enzyme inhibition. The highest yield was obtained in secang leaves water extract of 23,72%. The highest total flavonoid yield was shown by secang leaves water extract of 10,69 mg QE/g extract and kacang gude leaves of 5,74 mg QE/g extract. Inhibitory power testing of MAO-A compared to clorgyline as a control inhibitor (IC). The highest inhibitory power against MAO-A was produced by secang leaves water extract of 87,08% and kacang gude seeds of 76,06%. The percentage value of inhibition is more significant than clorgyline of 67,59%. It shows that the water extract from secang leaves and kacang gude seeds has activity as an antidepressant agent in vitro.
Keyword: antidepresan, flavonoid, kacang gude, MAO-A, secang

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