Judul: Determination of the total phenol and flavonoid levels and the antioxidant capacity of Kemuning leaf extract (Murraya paniculata L.)
Abstrak: Tanaman kemuning merupakan tanaman herbal yang berkhasiat sebagai antioksidan. Antioksidan dapat mencegah dan memperbaiki sel yang mengalami stres oksidatif dengan cara memutus rantai pembentukan radikal bebas. Penelitian ini bertujuan untuk mengetahui kadar flavonoid dan fenolik total Murraya paniculata serta kapasitas antioksidannya menggunakan metode DPPH (2,2-difenil-1-pikrihidrazil), FRAP (Ferric Reducing Antioxidant Capacity), dan ABTS (2,2’-azinobis-[3-ethylbenzothiazoline-6-sulfonic acid]). Kadar fenolik total dan flavonoid total yang terdapat pada tanaman kemuning adalah 16,9688 mg ekuivalen asam galat/g serbuk kering dan 4,7491 mg ekuivalen kuarsetin/g serbuk kering. Hasil penentuan kapasitas antioksidan ekstrak daun kemuning menggunakan metode DPPH, FRAP, dan ABTS secara berturut-turut adalah 2,004 ± 0,02; 8,09 ± 0,16; dan 23,36 ± 0,17 mg troloks/g serbuk kering. Kapasitas antioksidan ekstrak daun kemuning menggunakan metode ABTS menghasilkan kapasitas antioksidan terbesar dan berpotensi memiliki aktivitas antioksidan yang tinggi., Kemuning is a herbal plant that is efficacious as an antioxidant. Antioxidants can prevent and repair cells that experience oxidative stress by breaking free radical chain formation. This study aims to determine the levels of flavonoids and total phenolics in Murraya paniculata and their antioxidant capacity using the DPPH (2,2-diphenyl-1-pikrihidrazyl), FRAP (Ferric Reducing Antioxidant Capacity) and ABTS (2,2'-azinobis-[3-ethylbenzothiazoline-6-sulfonic acid]). The total phenolic and total flavonoid levels found in the yellowing plants were 16.9688 mg gallic acid equivalent/g dry powder and 4.7491 mg quercetin equivalent/g dry powder. The results of increasing the antioxidant capacity of the yellow leaf extract using the DPPH, FRAP, and ABTS methods were 2.004 ± 0.02; 8.09 ± 0.16; and 23.36 ± 0.17 mg troloks/g dry powder. The antioxidant capacity of yellow leaf extract using the ABTS method produces the largest antioxidant capacity and has the potential to have high antioxidant activity.
Keyword: antioxidant capacity, total phenolic, total flavonoid, murraya paniculata

Judul: Produksi Flavonoid Daun Kemuning (Murraya Paniculata (L.) Jack) Pada Dosis Pupuk Kandang Ayam Dan Abu Sekam Dengan Interval Panen Yang Berbeda
Abstrak: Kemuning telah digunakan secara tradisional sebagai tanaman obat karena mengandung metabolit sekunder yang memiliki fungsi sebagai antioksidan, antibakteri, analgesik, anti-inflamasi, penurun kadar kolesterol darah, anti-obesitas. Penerapan teknik budidaya dengan pemberian pupuk organik dan pengaturan interval panen dapat dilakukan untuk meningkatkan produksi metabolit sekunder khususnya senyawa flavonoid. Diduga terdapat perbedaan produksi flavonoid daun kemuning akibat pemberian kombinasi dosis pupuk kandang ayam dan abu sekam dengan interval panen yang berbeda. Penelitian ini bertujuan untuk mengetahui pengaruh pemberian kombinasi dosis pupuk kandang ayam dan abu sekam dengan interval panen yang berbeda terhadap produksi senyawa flavonoid tanaman kemuning. Percobaan lapangan dilakukan di Kebun Percobaan Organik IPB, Cikarawang dengan letak geografi antara 6o30' - 6o45' LS dan 106o30'-106o45' BT, pada 250 m di atas permukaan laut (dpl), Bogor, Indonesia, pada bulan Juni 2014 - Februari 2015. Penelitian menggunakan Rancangan Acak Kelompok (RAK) faktorial dengan 2 faktor, yaitu faktor 1 terdiri dari 8 kombinasi dosis pupuk kandang ayam (PA) dan abu sekam (AS) yaitu tanpa pupuk (kontrol), 0 kg PA + 3 kg AS; 7 kg PA + 0 kg AS; 7 kg PA + 3 kg AS; 14 kg PA + 0 kg AS; 14 kg PA + 3 kg AS; 21 kg PA + 0 kg AS; 21 kg PA + 3 kg AS per tanaman, dan faktor 2 adalah interval panen (2, 3, dan 4 bulan). Tinggi pangkas panen adalah 75 cm dari permukaan tanah. Percobaan dilakukan dengan 4 kali pengulangan. Hasil menunjukkan bahwa pemberian kombinasi dosis pupuk kandang ayam dan abu sekam dengan dosis yang berbeda tidak memberikan pengaruh nyata terhadap semua peubah yang diamati. Interval panen 4 bulan nyata meningkatkan produksi daun berupa bobot basah dan kering daun total masing-masing sebesar 914.92 dan 258.53 g.tanaman-1. Interval panen 4 bulan menghasilkan aktivitas enzim Phenylalanine Ammonia-lyase (PAL) (7.92 x 10-5 mg cinnamic acid eq.g bobot basah-1), produksi protein (7.96 mg.tanaman-1), flavonoid total (682.8 mg.tanaman-1), antosianin (1.178 mg.tanaman-1), dan aktivitas antioksidan (76.51%) tertinggi. Interaksi pemberian pupuk kandang dan abu sekam dengan interval panen tidak memberikan pengaruh terhadap semua peubah pengamatan.
Keyword: abu sekam, aktivitas antioksidan, Phenylalanine Ammonia-lyase, antosianin, klorofil, pupuk kandang ayam

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