Judul: Interaksi Genotipe dan Lingkungan, serta Stabilitas Ekstrak Etil Asetat 20 Genotipe Temu Ireng di Tiga Lokasi Berbeda
Abstrak: Temu ireng merupakan tumbuhan asal Indonesia yang biasa digunakan sebagai campuran jamu. Hingga saat ini belum ada varietas temu ireng yang diusulkan, karena terbatasnya informasi mengenai kualitas bahan baku. Stabilitas genotipe adalah kemampuan genotipe untuk hidup pada berbagai lingkungan yang berbeda dan fenotipenya tidak mengalami banyak perubahan di setiap lokasi percobaan. Tujuan dari penelitian ini adalah mengetahui interaksi genotipe dan lingkungan, serta stabilitas genotipe temu ireng terhadap kondisi lingkungan yang berbeda melalui indikator komponen fitokimia berupa senyawa fenolik dan aktivitas antioksidan (DPPH dan ABTS). Ekstrak etil asetat temu ireng diperoleh melalui ekstraksi basah. Pengujian total fenol dilakukan melalui metode Folin-Ciocalteu dan pengujian aktivitas antioksidan melalui metode DPPH dan ABTS. Hasil penelitian menunjukkan interaksi genotipe dan lingkungan 20 genotipe temu ireng dan tiga varietas temulawak di tiga lokasi (Bogor, Cianjur, Sukabumi) menghasilkan perbedaan yang nyata (p<0). Genotipe G13, G17, G18, G19, dan Cursina2 merupakan genotipe stabil berdasarkan senyawa fenol. Genotipe G3, G4, G6, G8, G9, G10, G11, G12, G13, G14, G15, G16, G17, G18, dan G20 merupakan genotipe stabil berdasarkan ekivalen troloks DPPH. Genotipe G2, G3, G7, G9, G11, G12, G13, G15, G19, dan G20 merupakan genotipe stabil berdasarkan ekivalen troloks ABTS. Genotipe yang stabil di setiap pengujian adalah genotipe G13.
Keyword: genotipe, interaksi genotipe dan lingkungan, lingkungan, stabilitas, temu ireng

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