Judul: Cacing ektoparasit pada kura-kura (Cuora amboinensis) air tawar di Daerah Banten
Abstrak: Parasit merupakan organisme yang hidup pada atau di dalam tubuh organisme lain (inang). keberadaan parasit dapat mengganggu kehidupan inang. Akibat yang paling fatal bila tetjadi infestasi secara besar-besaran dapat menyebabkan kemalian bagi inang tersebut. Pada penelitian ini dilakukan inventarisasi dan identifikasi cacing ektoparasit pada kura-kura air tawar yang diperoleh dari pedagang pengumpul di daerah Banten. Dari 189 ekor kura-kura yang terkumpul, 23 ekor (12.2 %) terinfestasi oleh cacing kelas Trematoda ordo Monogenea. Ditemukan 62 ekor cacing ektoparasit, 35 ekor dari karapas, 25 ekor dari plastron dan 2 ekor dari leher. Hasil identifikasi menunjukkan bahwa 62 ekor cacing tersebut tergolong dalam 4 superfamili yaitu Gyrodactiloidea, Tetraoncoidea, Acanthocotyloidea dan Dactylogyroidea. Identifikasi sampai famili hanya dapat dilakukan pada superfamili Tetraoncoidea dan Acanthocotyloidea. Lokasi penangkapan tidak menunjukkan adanya hubungan dengan jenis cacing yang menginfestasi. Prevalensi parasit pada kura-kura sebesar 100 % dan intensitas infestasi sebesar 4.21 parasit lindividu inang
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Judul: Inventarisasi dan identifikasi tungau ektoparasit pada cicak di Bogor
Abstrak: Organisme ektoparasit yaitu organisme yang hidup di permukaan tubuh inangnya, menghisap darah atau memakan rambut, kulit, atau sekresi kulit. Ahli parasit reptilia menemukan tempat perkembangbiakan ektoparasit pada reptilia, namun informasi mengenai tungau ektoparasit pada anggota reptilia khususnya cicak di Indonesia belum ada. Penelitian ini merupakan penelitian pertama di Indonesia. Penelitian ini bertujuan untuk menginventarisasi dan mengidentifikasi tungau ektoparasit yang menginfestasi cicak di wilayah Bogor. Cicak dikoleksi di wilayah Bogor Tengah dan Bogor Barat dengan menggunakan metode road sampling. Tungau ektoparasit diambil menggunakan sonde kemudian diawetkan dalam alkohol 70%. Pembuatan preparat tungau menggunakan metode sediaan utuh dan preparat tungau dijernihkan di laktofenol. Hasil penelitian menunjukkan bahwa tungau ektoparasit melekat di bagian kepala, ketiak, jari depan, dan belakang, pangkal paha, ekor badan dan telinga cicak Cosymbotus platyurus, Hemidactylus frenatus, dan Hemidactylus garnotii. Tungau tersebut berwarna jingga tua ketika ditemukan di beberapa bagian tubuh cicak. Ciri dari tungau tersebut yaitu memiliki skutum dorsal tubuh dan banyak rambutrambut pendek di bagian dorsal tubuh.Tungau tersebut termasuk dalam Famili Pterygosomatidae, Genus Geckobia. Infestasi tungau tertinggi pada C. platyurus yaitu di bagian telinga (49 tungau), sedangkan pada H. frenatus (191 tungau) dan H. garnotii (10 tungau) adalah di jari belakang. Nilai tertinggi rataan infestasi tungau tiap cicak ada pada H. frenatus (18.2).
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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