Judul: Kandungan Flavonoid dan Aktivitas Antioksidan 20 Genotipe Rimpang Temu Ireng pada Musim Tanam Kedua
Abstrak: Temu ireng (Curcuma aeruginosa Roxb.) merupakan salah satu jenis tanaman golongan temu-temuan (Zingiberaceae) yang mengandung metabolit sekunder seperti flavonoid. Rendahnya produksi temu ireng disebabkan karena belum tersedianya varietas unggul dari temu ireng. Penelitian ini bertujuan menentukan genotipe terbaik dari total flavonoid dan aktivitas antioksidan yang dilihat dari segi stabilitas dan daya adaptasi dari 20 genotipe temu ireng yang ditanam pada tiga lokasi yang berbeda yaitu di Bogor, Sukabumi dan Cianjur. Tahapan penelitian meliputi pengujian kadar flavonoid dan aktivitas antioksidan metode FRAP serta daya adaptasi genotipe temulawak pada penanaman multilokasi dengan metode AMMI (Additive Main Effect and Multiplicative Interaction). Produktivitas kadar total flavonoid tertinggi dihasilkan sebesar 1.8980 mg QUE/g bobot kering di lokasi Bogor dan genotipe G4 sebesar 1.6170 mg QUE/g bobot kering. Nilai aktivitas antioksidan tertinggi dihasilkan di lokasi Bogor sebesar 18.5959 µmol TE/g bobot kering, dan genotipe G1 sebesar 30.6045 µmol TE/g bobot kering. Keragaman genotipe berpengaruh nyata terhadap kadar total flavonoid, akan tetapi tidak berpengaruh nyata pada aktivitas antioksidan. Interaksi lokasi dan genotipe tidak berpengaruh nyata terhadap kedua parameter uji. Genotipe yang stabil pada ketiga lokasi (Bogor, Cianjur, Sukabumi) adalah G5, G7, G8, G9, G17, G18 untuk kadar flavonoid, serta G1, G3, G4, G5, G7, G8, G10, G18, dan G20 untuk aktivitas antioksidan. Kata kunci: temu ireng, genotipe, flavonoid, antioksidan, stabilitas, Temu ireng (Curcuma aeruginosa Roxb.) is a type of plant that belongs to the group of plants belonging to the curvature family (Zingiberaceae) which contains secondary metabolites such as flavonoids. The low production of temu ireng is due to the unavailability of superior varieties of temu ireng. This study aims to determine the best genotype of total flavonoid and antioxidant activity in terms of stability and adaptability of 20 genotypes of temu ireng grown in three different locations, namely in Bogor, Sukabumi and Cianjur. The research stages included testing the levels of flavonoids and antioxidant activity using the FRAP method as well as the adaptability of the temulawak genotype in multilocation planting using the AMMI method. The highest productivity of total flavonoid content was 1.8980 mg QUE/g dry weight at the Bogor location and the G4 genotype was 1.6170 mg QUE/g dry weight. The highest antioxidant activity value was obtained at the Bogor location of 18.5959 mol TE/g dry weight, and the G1 genotype was 30.6045 mol TE/g dry weight. Genotype diversity had a significant effect on total flavonoid levels, but had no significant effect on antioxidant activity. The interaction of location and genotype did not significantly affect the two test parameters. Stable genotypes at the three locations (Bogor, Cianjur, Sukabumi) were G5, G7, G8, G9, G17, G18 for flavonoid content, and G1, G3, G4, G5, G7, G8, G10, G18, and G20 for activity antioxidants. Keywords: Curcuma aeruginosa, genotype, flavonoids, antioxidant, stability
Keyword: temu ireng, genotipe, flavonoid, antioksidan, stabilitas

Judul: Phenolic total and Antioxidant Activity of 20 Genotype Temu Ireng in the Second Growing Season
Abstrak: Temu ireng (Curcuma aeruginosa Roxb) merupakan tanaman obat yang termasuk dalam famili Zingibaracae. Varietas unggul temu ireng harus stabil dan dapat tumbuh di berbagai kondisi lingkungan. Penelitian ini bertujuan untuk mengevaluasi total fenolik dan aktivitas antioksidan melalui interaksi antara genotipe dan lingkungan (GEI) dari 20 genotipe ekstrak etanol rimpang temu ireng yang ditanam pada musim tanam kedua dan tumbuh di tiga lingkungan agroekologi yang berbeda. Pengujian total fenolik menggunakan metode Folin Ciocalteu. Genotipe yang stabil pada pengujian total fenolik adalah G1, G2, G3, G4, G6, G12, G13, dan G16. Pengujian aktivitas antioksidan menggunakan metode DPPH. Genotipe yang stabil aktivitas antioksidannya adalah G1, G5, G6, G13, G16, dan G17. Genotipe unggul yang stabil di ketiga lokasi pada kedua parameter adalah G1, G6, G13, dan G16., Temu ireng (Curcuma aeruginosa RoxB) is a medicinal plant that belongs to the Zingibaracae family. Superior varieties of temu ireng should be stable and can be grown in various environmental conditions. This research was conducted to evaluate the total phenolic and antioxidant activity through Genotype by Environment Interaction (GEI) of 20 genotypes of ethanol extract temu ireng rhizomes that grown in the second growing season and grown in three different agro-ecological environments. Total phenolic was tested using the Folin-Ciocalteu method. The stable genotypes of total phenolic are G1, G2, G3, G4, G6, G12, G13, and G16. Antioxidant activity was tested using the DPPH method. The stable genotypes of antioxidant activity are G1, G5, G6, G13, G16, and G17. The stable superior genotypes in all three locations on both parameters are G1, G6, G13, and G16.
Keyword: DPPH, Stabilitas genotipe, Temu Ireng, Total Fenolik

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