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: Analisis Stabilitas Genotipe Temu Ireng dari Tiga Lokasi Berdasarkan Total Flavonoid, Saponin, dan Aktivitas Antioksidan
Abstrak: Temu ireng memiliki potensi yang baik untuk diaplikasikan secara luas sebagai obat tradisional, namun pemanfaatannya belum optimal karena tingkat produktivitasnya yang masih rendah. Tujuan penelitian yaitu menentukan genotipe tanaman temu ireng yang stabil dan memiliki aktivitas yang baik melalui pengaruh interaksi genotipe x lingkungan berdasarkan kandungan flavonoid, saponin, serta aktivitas antioksidan. Penelitian menggunakan 20 genotipe temu ireng sebagai sampel serta 2 varietas temu lawak kontrol yang ditanam pada 3 lokasi berbeda (Bogor, Cianjur dan Sukabumi). Pengujian total flavonoid dilakukan menggunakan metode AlCl3, sementara pengujian total saponin dilakukan menggunakan metode vanilin-asam sulfat, dan pengujian aktivitas antioksidan dilakukan menggunakan metode CUPRAC. Hasil penelitian menunjukkan adanya interaksi nyata antara faktor genotipe dan lingkungan pada taraf probabilitas 0%. Kandungan total flavonoid seluruh sampel pada ketiga lokasi tidak berbeda secara signifikan yang berkisar pada 0.02 hingga 0.04 mg QUE/g berat basah dan seluruh sampel merupakan genotipe stabil. Genotipe G1 dan G6 menghasilkan aktivitas antioksidan tertinggi secara signifikan pada ketiga lokasi sebesar 11.501 dan 10.201 mol TE/g berat basah, sementara seluruh sampel kecuali kedua genotipe tersebut merupakan genotipe stabil. Genotipe G5 mengandung total saponin tertinggi secara signifikan pada ketiga lokasi sebesar 146.369 mg DE/g berat basah, sementara genotipe G1, G2, G15, dan G16 merupakan genotipe stabil., Temu ireng has good potential to be widely applied as traditional medicine, but its’ utilization is not yet optimal due to its’ low productivity. The purpose of this study is to determine stable genotypes of temu ireng which had good activity through genotype and environmental interactions based on the content of flavonoids, saponins, and antioxidant activity. This study uses 20 genotypes of temu ireng as samples and 2 varieties of temu lawak as controls which were planted in 3 different locations (Bogor, Cianjur and Sukabumi). Total flavonoid testing was carried out using the AlCl3 method, while total saponin testing was carried out using the vanillin-sulfuric acid method and the antioxidant activity testing was carried out using the CUPRAC method. The results showed that there was a significant interaction between genotype and environmental factors at probability level of 0%. The total flavonoid content of all samples at the three locations did not differ significantly between 0.02 to 0.04 mg QUE/g fw, and all samples were stable genotypes. Genotypes G1 and G6 produced the highest antioxidant activity significantly at the three locations at 11.501 and 10.201 mol TE/g fw, while all samples except the two genotypes were stable genotypes. Genotype G5 had the highest total saponin content significantly at the three locations at 146.369 mg DE/g fw, while genotypes G1, G2, G15, and G16 were stable genotypes.
Keyword: antioxidant, flavonoid, genotype x environment interaction, saponin, temu ireng

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