Judul: Fenotipe saluran ceerna beruk (Macaca nemestrina) sebagai hewan model penelitian
Abstrak: Pig-tailed macaques (Macaca nemestrina) are primates where its original habitat is on the island of Sumatera, Indonesia. The local name in Indonesia is Beruk. Beruk has special characters, with big body and steady walk on four legs and has already obvious its sexual dimorphism. The Beruk has a tail which is about 1/3 part of its body length while its head has no diceernible neck. Its has brown fur.
Keyword: 

Judul: Perbandingan Preferensi Beruk (Macaca nemestrina) Usia Muda terhadap Perangkat Pengayaan Lingkungan di Pusat Studi Satwa Primata IPB
Abstrak: Beruk (Macaca nemestrina) adalah satwa primata endemis Indonesia dan digunakan sebagai salah satu hewan model dalam penelitian. Pemanfaatan beruk dalam dunia biomedis harus mengacu pada prinsip kesejahteraan hewan. Pemeliharaan beruk pada penangkaran ex situ atau fasilitas buatan berpotensi memengaruhi perilakunya menjadi abnormal, sehingga pengayaan lingkungan penting untuk mempertahankan perilaku alaminya. Penelitian ini bertujuan mengetahui respon beruk terhadap pemberian objek baru yang dapat dijadikan sarana pengayaan lingkungan yang memperkaya aktivitas satwa sehari-hari. Penelitian ini menggunakan tujuh ekor beruk berkoloni usia 1 – 2 tahun. Pengamatan perilaku dilakukan saat pemberian perangkat pengayaan lingkungan berupa puzzle ball dan rumput sintetis sebagai sarana pemberian sayuran. Perilaku yang diamati adalah eating, exploring, foraging, socializing, aggressiveness, fighting, playing, dan inactive. Berdasarkan studi ini dapat disimpulkan bahwa pengamatan perilaku Macaca nemestrina di Pusat Studi Satwa Primata IPB yang diberikan perangkat pengayaan lingkungan berupa puzzle ball dan rumput sintetis menunjukkan perilaku yang didominasi perilaku eating, exploring, dan foraging. Pemberian perangkat pengayaan akan mendorong sifat alamiahnya pada beruk, khususnya perilaku foraging dan exploring. Sementara, perilaku yang teramati tanpa pemberian perangkat pengayaan didominasi perilaku inactive dan playing., Pigtail macaque (Macaca nemestrina) is an endemic primate of Indonesia and is used as one of the model animals in research. The use of macaques in the biomedical world must refer to the principles of animal welfare. The maintenance of macaques in ex situ captivity or artificial facilities has the potential to influence their behavior to become abnormal, so environmental enrichment is important to maintain their natural behavior. The purpose of this research is to determine the ape's response to giving new objects that can be used as a means of enriching the environment that enriches the daily activities of animals. This study used seven macaques in colonies aged 1 – 2 years. Behavioral observations were made when giving environmental enrichment in the form of puzzle balls and synthetic grass as a means of giving vegetables. The observed behaviors were eating, exploring, foraging, socializing, aggressiveness, fighting, playing and silent. Based on this study, it can be concluded that observations of the behavior of Macaca nemestrina at the Primate Research Center of IPB University, which were given environmental enrichment, showed behavior that was dominated by eating, exploring, and foraging behavior. The provision of enrichment will encourage their natural properties in macaques, especially foraging and exploring behavior. Meanwhile, the observed behavior without the provision of enrichment was dominated by silent and playing behavior.
Keyword: perilaku, kesejahteraan hewan, foraging, exploring

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