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: Positive Reinforcement Training sebagai Upaya Refinement Pemeriksaan Fisik Macaca nemestrina Usia Muda di Kandang Koloni
Abstrak: Beruk (Macaca nemestrina) merupakan satwa primata yang dapat berperan sebagai hewan model dalam penelitian biomedis. Penerapan kesejahteraan hewan dalam pemeliharaan dan penggunaan beruk dapat diterapkan melalui positive reinforcement training (PRT) sebagai upaya refinement dalam pemeliharaan kesehatan, termasuk mendukung tindakan pemeriksaan fisik. Penelitian ini bertujuan membentuk tingkah laku kooperatif pada beruk usia muda untuk memudahkan pelaksanaan pemeriksaan fisik. Penelitian dilakukan pada empat ekor beruk jantan di kandang koloni, dengan melakukan proses habituasi, temperament assessment (TA), pengamatan perilaku dengan metode focal sampling (FS), menggunakan novel object untuk mengidentifikasi potensi hewan untuk dilatih, dan PRT untuk membentuk perilaku yang mendukung pemeriksaan fisik berupa palpasi. Penilaian TA dilakukan untuk mengategorikan temperamen beruk sebagai temperamen open, moderate, dan inhibited sehingga dapat mengkaji potensi hewan untuk dilatih. Tahapan PRT meliputi enam tahap target training, dilanjutkan dengan dua tahap position training. Hasil TA menunjukkan bahwa dua ekor beruk memiliki temperamen open, satu ekor temperamen moderate, dan satu ekor temperamen inhibited. Hasil penelitian menunjukkan bahwa tiga ekor beruk dengan temperamen open dan moderate berhasil mencapai tahap akhir PRT dan satu ekor beruk temperamen inhibited tidak berhasil menyelesaikan PRT. Namun, terlihat adanya inkonsistensi karena sifat beruk usia muda yang playful dan pengaruh faktor distraksi dari sekitar kandang., Pigtailed-macaque (Macaca nemestrina) is a nonhuman primate that is distributed in Southeast Asia. These primates serve as an animal model in biomedical research. Implementation of animal welfare in the care and use of this animal may be through positive reinforcement training (PRT) as a refinement for conducting physical examinations. The objective of this research was to form cooperative behavior in adolescent pigtailed macaques by PRT to facilitate the implementation of physical examinations without restraint. The study was performed on four adolescent male pigtail macaques housed in colony setting. The study comprised of a habituation process, behaviour observation using focal sampling and temperament assessment using novel objects to identify trainability of animals, and PRT to shape behavior that will support palpation examination without restraint. Temperament assessment categorized animals into open, moderate, and inhibited. The result showed that the temperaments of two were open, one was of moderate temperament, and one of inhibited temperament. The training stages in PRT include six stages of target training followed by two stages of position training. The study resulted in three macaques (of open and moderate temperament) completed all training stages where as one macaque (inhibited temperament) did not. The PRT process in adolescent macaques showed inconsistent progress, which may be due to the playful nature of pigtailed macaques at this age and other factors relevant to social grouping setting of the animals.
Keyword: kesejahteraan hewan, positive reinforcement training, refinement, pemeriksaan fisik, beruk

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