Judul: Inventarisasi hama pada tanaman krisan (Chrysanthemum morifolium Rem.) di desa Cihideung, Kec. Cisarua Kabupaten Bandung Jawa Barat
Abstrak: Tujuan survei hama dan penyakit ini adalah untuk menginventarisasi hama-hama yang menyerang tanaman krisan dan menghitung intensitas serta luas serangannya. Metode yang digunakan yaitu metode wawancara dan pengamatan di lapang. Jenis tanaman krisan yang diamati yaitu krisan merah dan krisan kuning. Pengamatan untuk jenis krisan merah dilakukan pada pertanaman belum berbunga dan sedang berbunga yang ditanam secara monokultur dan tumpangsari dengan bawang daun. Pengamatan untuk jenis krisan kuning hanya dilakukan pada pertanaman sedang berbunga yang ditanam secara monokultur. Serangga-serangga pengganggu yang ditemukan adalah Macrosiphoniella sanborni Gill. (Homoptera: Aphididae), Aphis gossypii Glov. (Homoptera: Aphididae), tungau merah (Acarina: Tetranychidae), Leucothrips sp. (Thysanoptera: Thripidae), Phytomyza syngenesiae Hardy (Diptera: Agromy- zidae) dan ulat jengkal (Lepidoptera: Geometridae) yang menyerang daun serta penggerek bunga (Lepidoptera: Pyralidae) dan pemakan bunga (Lepidoptera: Noctuidae)…
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Judul: Komunitas arthropod pada tanaman krisan (Chrysanthemum sp.) di saung Mirwan, Bogor
Abstrak: Penelitian ini ditujukan untuk menginventarisasi jenis-jenis arthropod pada tanaman krisan potong dan krisan pot sebagai dasar pengembangan Pengendalian Hama Terpadu. Penelitian ini dilaksanakan pada bulan Maret sampai April 1995. Pengamatan langsung dilakukan seminggu sekali pada pembibitan, tanaman stadia vegetatif I, vegetatif II, generatif I dan generatif II. Jumlah tanaman yang diamati masing-masing sebanyak 20 tanaman krisan pot dan 20 tanaman krisan potong untuk setiap stadia per minggu pengamatan. Komunitas arthropod yang ditemukan pada tanaman krisan pot dan krisan potong dapat digolongkan menjadi hama, musuh alami dan serangga lain. Komunitas arthropod pada tanaman krisan potong dan krisan pot setiap stadia tanaman secara umum tidak berbeda dan didominasi oleh golongan hama. Keragaman arthropod pada krisan potong lebih tinggi dibanding krisan pot. Pola penyebaran arthropod pada krisan potong dan krisan pot menunjukkan bahwa populasi hama yang ada di dalam rumah kaca lebih besar daripada populasi musuh alami dan serangga lain. Hama yang ditemukan pada tanaman krisan potong dan krisan pot umumnya sama. Hama tersebut adalah agas-agas akar (Diptera: Sciaridae), agas-agas bungkul (Diptera: Cecidomyiidae), lalat bersayap gambar (Diptera: Otitidae), pengorok daun (Diptera: Agromyzidae), belalang (Valanga nigricornis Burm. [Orthoptera: Acrididae]), pelompat daun (Homoptera: Cicadellidae), kutu loncat (Homoptera: Psyllidae), kutudaun (Homoptera: Aphididae), lalat putih (Homoptera: Aleyrodidae), thrips (Thysanoptera: Thripidae), ulat daun (Spodoptera sp. [Lepidoptera: Noctuidae]) dan ulat penggerek bunga (Lepidoptera: Pyralidae).
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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