Judul: Pemilihan Makanan Oleh Monyet Karier Buta Warna
Abstrak: Secara fisiologis, monyet ekor panjang karier butawarna mengalami kesulitan melihat warna merah. Respons fisiologis monyet karier butawarna terhadap warna merah berada di antara monyet normal dan butawarna. Penelitian pemilihan makanan oleh monyet karier butawarna di Taman Wisata Alam Pangandaran, Ciamis, Jawa Barat dilaksanakan bulan Agustus sampai Oktober 2000. Pengumpulan data menggunakan metodefocal animal sampling, scan sampling, dan ad libitum. Scan sampling menggunakan interval waktu 30 detik.
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Judul: Preferensi Pakan Monyet Ekor Panjang (Macaca fascicularis Raffles 1821) di Kampus IPB Dramaga
Abstrak: Monyet ekor panjang (Macaca fascicularis) akan memperluas wilayah jelajah hariannya untuk memperoleh sumber pakan yang cukup hingga melimpah, sehingga tak jarang menimbulkan konflik. Identifikasi preferensi pakan monyet ekor panjang perlu dilakukan sebagai salah satu bentuk meminimalkan konflik. Penelitian ini bertujuan untuk mengidentifikasi jenis pakan dan preferensi pakan, serta menganalisis perilaku harian monyet ekor panjang. Penelitian dilakukan dengan analisis vegetasi dan observasi lapang terhadap perilaku harian monyet ekor panjang. Hasil penelitian menunjukkan terdapat sebanyak 22 jenis tumbuhan pakan dan 2 jenis pakan non-tumbuhan. Monyet ekor panjang merupakan satwa frugivore, namun akan akan bersifat opportunistic omnivore apabila ketersediaan buah berkurang. Monyet ekor panjang lebih tertarik memakan buah, terutama buah belimbing bintang (Averrhoa carambola), buah matoa (Pometia pinnata), dan buah jabon (Neolamarckia cadamba),. Perilaku harian monyet ekor panjang yang paling dominan pada tiap petak contoh yakni bergerak, diam, dan makan., Long-tailed monkey (Macaca fascicularis) will expand their daily home range to obtain sufficient and abundant food sources, thus often causing conflict. Identification of long-tailed monkey feed preferences needs to be done as a form of minimizing conflict. This research aims to identify the type of feed and feed preference, also to analyze the daily activity of long-talide monkey’s. The research was conducted by vegetation analysis and field observation of the daily acyivity of long-tailed monkey’s. The results of this research showed that there were 22 types of feed plants and 2 types of non-plant feeds. Long-tailed monkeys are kind of fructivore animals, but will be ooportunistic omnivore if the avaibility of fruits decrease. Long-tailed monkeys are more interested to eat fruit, especially strafruit fruit (Averrhoa carambola), matoa fruit (Pometia pinnata), and jabon fruit (Neolamarckia cadamba). The most dominant daily activity of long-tailed monkey’s in each plot sampling were move, stay still, and eat.
Keyword: food preference, long-tailed monkey, plot sampling

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