Judul: Induksi mutasi krisan (Dendranthema grandiflora tzvelev.) melalui iradiasi planlet
Abstrak: Penelitian ini dilakukan dengan tujuan mendapatkan mutan krisan melalui perlakuan iradiasi sinar gamma dengan bahan planlet. Penelitian dilaksanakan di Instalasi Penelitian Tanaman Hias Cipanas Jawa Barat dan Pusat Penelitian dan Pengembangan Teknologi Isotop dan Radiasi Badan Tenaga Nuklir Nasional (BATAN) Pasar Jum'at Jakarta darı Bulan September 1999 sampai September 2000. Penelitian menggunakan Rancangan Acak Lengkap pola faktorial dengan dua faktor dan tiga ulangan. Faktor pertama adalah genotipe krisan yang terdiri atas tujuh varietas: Saraswati, Sekartaji, Dewi Sartika, Kartini, Chandra Kirana, Larasati dan Cut Nyak Dien. Faktor kedua adalah dosis iradiasi sinar gamma dengan delapan taraf, yaitu 0, 10, 12.5, 20, 25, 30, 35, dan 40 Gy. Iradiasi sinar gamma dilakukan dua kali dengan dosis yang berbeda. Iradiasi sinar gamma pertama diaplikasikan pada planlet krisan yang berumur 30 hari sejak subkultur terakhir dengan dosis 0, 10, 12.5, 20, 25, 30, 35 dan 40 Gy. Sehari setelah aplikasi sinar gamma, seluruh planlet disubkulturkan dalam media Murashige and Skoog yang mengandung 0,1 mg/l IAA dan disimpan di dalam ruang kultur. Setelah 8 minggu sejak subkultur, planlet yang hidup diradiasi kembali dengan dosis 15 Gy. Kemudian disubkulturkan kembali dalam media Murashige and Skoog yang mengandung 0,1 mg/L IAA dan disimpan dalam laboratorium kultur jaringan selama 8 minggu. Subkultur dilakukan dengan cara menanam tiap ruas batang yang masing-masing memiliki satu mata tunas aksilar. Selanjutnya seluruh planlet diaklimatisasikan selama 2 minggu di rumah kaca. Setelah itu planlet ditanam dalam polybag berdiameter 23 cm dan dipelihara hingga berbunga. Peubah yang diamati meliputi persentase kematian tanaman, tinggi tanaman, jumlah ruas, lebar daun, jumlah bunga, diameter bunga, diameter tangkai bunga, frekuensi kejadian mutan dan penyimpangan morfologi vegetatif…dst
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Judul: Induksi Keragaman Dua Varietas Krisan (Dendranthema grandiflora Tzvelev) Dengan Iradiasi Sinar Gamma Secara In Vitro
Abstrak: The research aims were to study the effect of gamma irradiation doses on Chrysanthemum (Dendranthema grandiflora Tzvelev) and increasing the genetic diversit. The research were conducted at IPB Tissue Culture Laboratory, BATAN Laboratory, and IPB Micro Tehnique Laboratory, during February–November 2010. This research used completely randomized design with two factors. The first factor is varieties of Chrysanthemum, including Dewi Ratih and Puspita Nusantara. The second factor is five doses of gamma irradiation (0, 20, 40, 60, 80 Gy). The result showed that the interaction between varieties with doses of gamma irradiation affected growth of plants and the number of chloroplast in guard cell. The number and size (length and width) of stomata were influenced by a varieties and and irradiation dose as a single factor. Increasing doses of gamma irradiation could increase the length and width of stomata. LD50 of krisan planlets were obtained 22.22 Gy for Dewi Ratih and 22.85 Gy for Puspita Nusantara. The gamma radiation dose 20 Gy produced 13 potential mutant on Dewi Ratih and Puspita Nusantara varieties. Putative mutants characteristic showed small leaves and no serrated, reddish stems, stunted, rosette, and variegata leaves.
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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