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: In Silico Screening of Active Compounds from Fabaceae Family as Antidepressant with Exponential Consensus Ranking
Abstrak: Depresi sudah umum dalam istilah psikologis, namun masih sulit dimengerti dalam istilah molekuler. Secara molekuler, depresi disebabkan dengan meningkatnya aktivitas enzim monoamina oksidase A (MAO-A). Penemuan obat antidepresan baru dapat dicari dengan memanfaatkan tanaman obat yang tersedia secara konvensional. Tanaman keluarga Fabaceae, yaitu secang (Caesalpinia sappan), asam jawa (Tamarindus indica), putri malu (Mimosa pudica), dan kacang gude (Cajanus cajan) telah ditapis menggunakan metode in silico dengan pemeringkatan konsensus eksponensial (ECR). Pemeringkatan menggunakan ECR telah meningkatkan rasionalitas pemeringkatan dengan menggunakan lima piranti lunak dibanding penapisan maya dengan satu piranti lunak. Meskipun tidak semua piranti lunak memiliki performa yang baik untuk semua struktur dan ligan, ECR telah berhasil menyeleksi sembilan senyawa berpotensi aktif dengan nilai IC50 eksperimental yang juga diduga aktif sebagai inhibitor MAO-A. Penyebaran sembilan senyawa paling banyak terdapat pada tanaman asam jawa dan kacang gude. Tanaman tersebut diduga berpotensi sebagai antidepresan didukung dengan data rendemen penelitian sebelumnya., Depression is already common in psychological terms, but it’s kind of poorly understood in molecular terms. On a molecular level, depression is caused by increased activity of the enzyme monoamine oxidase A (MAO-A). Medicinal plants can be utilized as a conventional alternative for developing new antidepressant drugs. Plants that are from Fabaceae family such as secang (Caesalpinia sappan), asam jawa (Tamarindus indica), putri malu (Mimosa pudica), and kacang gude (Cajanus cajan) have been screened using in silico method with exponential consensus ranking. Exponential consensus ranking (ECR) has improved the rationality of the ranking by using five docking software compared to virtual screening using one docking software. Although not all docking software outperform all structures and ligands, ECR has successfully selected nine potentially active compounds with experimental IC50 values that are also suspected to be active as MAO-A inhibitor. The most abundant distribution of these nine compounds are found in asam jawa and kacang gude plants. These plants are suspected to have potential as antidepressants, supported by previous yield data.
Keyword: antidepresan, depresi, ECR, Fabaceae, MAO-A, antidepressant, depression

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