Judul: Studi daya hantar listrik dan hubungannya dengan mutu fisiologis benih cabai (Capsicum annuum L.)
Abstrak: Penelitian ini dilaksanakan di Laboratorium Fisika dan Laboratorium Biologi, Balai Besar Pengembangan Pengujian Mutu Benih Tanaman Pangan dan Hortikultura, Departemen Pertanian, Cimanggis, Depok; dan Laboratorium Ilmu dan Teknologi Benih, Fakultas Pertanian, Institut Pertanian Bogor. Penelitian ini bertujuan: (1) mempelajari beberapa faktor (suhu ruang pengujian, lama perendaman dan kadar air benih) yang diduga berpengaruh terhadap hasil pengujian daya hantar listrik benih cabai (Capsicum annuum L.) serta kaitannya dengan vigor benih; dan (2) mengevaluasi kemungkinan penggunaan uji daya hantar listrik untuk mendeteksi tingkat vigor benih cabai berdasarkan korelasinya dengan beberapa tolok ukur vigor lain.
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Judul: Analysis of seed vigor in relation to storability of pepper (Capsicum annuum L.) Seedand its genetic parameter estimation
Abstrak: Kemampuan benih cabai dalam mempertahankan mutu benihnya di penyimpanan atau daya simpan benih cabai merupakan kendala vigor benih di pemasaran.Daya simpan benih cabai ditentukan oleh vigor benih. Metode pengujian vigor benih cabai yang mudah, cepat, efisien dan bjektif belum banyak diteliti. Vigor benih cabai dikendalikan oleh faktor genetik, dan vigor adalah sifat kuantitatif yang dipengaruhi oleh lingkungan selama pembentukan benih, panen dan penyimpanan. Penelitian ini mencakup tiga percobaan yaitu (1) Metode pengusangan cepat untuk pengujian vigor daya simpan benih cabai ((Capsicum annuun L.), (2) Konsistensi metode pengujian vigor daya simpan benih cabaidan (3) Pendugaan parameter genetik vigor daya simpan benih cabai menggunakan analisis silang halfdialel. Percobaan pertama bertujuan mendapatkan metode pengujian vigor benih cabai yang mudah, cepat, tepat dan objektif khususnya vigor daya simpan. Benih tanamanyang digunakan adalah benih cabai (Capsicum annuum L) koleksi Bagian Genetika dan Pemuliaan Tanaman Institut Pertanian Bogor (IPB) yang baru dipanen. Metode percobaan menggunakan Rancangan Kelompok Lengkap Teracak (RKLT) dengan satu faktor. Faktornya adalah metode pengujian vigor daya simpan benih cabai. Metode perconaannya terdiri dari 5 metode yaitu 1 metode deteriorasi alami (natural deterioration pada suhu kamar dan RH terkontrol(metode pembanding), dengan periode 0, 3, 6, 9 dan 12 bulan dengan 4 metode pengusangan cepat yang akan dibandingkanya itu (1) metode pengusangan cepat dengan air panas 60ºC dengan periode 0, 60. 120, 180 dan 240 menit,(2) metode pengusangan cepat dengan larutan metanol 20% dengan periode 0, 2, 4, 6 dan 8 jam, (3) metode pengusangan dengan larutan etanol 20% dengan periode 0, 2, 4, 6 dan 8 jam, dan (4) metode deteriorasi terkontrol pada suhu tinggi 40°C, dengan periode 0,24,48,72 dan 96 jam.
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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