Judul: Karakteristik Habitat dan Populasi Monyet Butung (Macaca ochreata) di Suaka Margasatwa Tanjung Peropa, Sulawesi Tenggara.
Abstrak: Macaca ochreata merupakan salah satu primata endemik Sulawesi, yang penyebarannya terdapat di Sulawesi Tenggara. Saat ini penelitian mengenai Macaca ochreata masih sangat sedikit, tercatat hanya ada satu penelitian mengenai habitat dan populasi Macaca ochreata di Suaka Margasatwa Tanjung Peropa (SMTP) yang dilakukan 35 tahun yang lalu oleh Bismark (1982) sehingga diperlukan data terbaru mengenai habitat dan populasi Macaca ochreata di SMTP. Karakteristik habitat di identifikasi dengan analisis vegetasi dan dugaan populasi dilakukan menggunakan metode concentration count. Penelitian ini dilakukan pada bulan Februari-Maret 2017 di Blok Hutan Kalobo dengan tujuan untuk mengidentifikasi karakteristik habitat dan menduga populasi Macaca ochreata. Indeks Nilai Penting (INP) tertinggi tingkat pohon yaitu Pangium edule, Artocarpus elastica dan Meliostoma nitida yang merupakan sumber pakan. Terdapat 33 spesies dari 17 famili tumbuhan pakan Macaca ochreata di lokasi penelitian. Macaca ochreata memanfaatkan pohon dengan stratum B dan stratum C. Sebanyak 4 kelompok Macaca ochreata yang ditemui terdiri dari 56 individu dan nilai kepadatan populasi Macaca ochreata sebesar 22,4 ind /km2.
Keyword: Habitat, Macaca ochreata, Populasi, Sulawesi Tenggara

Judul: Habitat characteristics and population size of black crested macaque (Macaca nigra Desmarest, 1822) in several habitat types in Tangkoko Nature Reserve, North Sulawesi
Abstrak: This study was conducted in Tangkoko Nature Reserve Bitung-North Sulawesi from July to August 2009. The objectives of this study were to identify habitat characteristics of Macaca nigra including physical component, vegetation structure, canopy cover, water availability, food availability, and to assess the population size in Tangkoko Nature Reserve. Vegetation analysis were made in five habitat types including beach forest, primary lowland forest, secondary lowland forest, transitional (ecotone) forest and burned forest. Fifty seven plant species from 29 families including seedling, sapling, pole, and tree were identified during this study. Population size was determined by direct encounter. Trees from family Anacardiaceae, Moraceae, Myrtaceae, Verbenaceae, and Clusiaceae were dominant in study area. The highest Diversity Index was found in lowland transitional forest. Similarity Index shown that every habitat types had little similarity, the biggest similarity index was found between primary forest and transitional (43.10 %). Canopy stratum C (4 - 20 m) was dominant in the study area. The most complex canopy strata were found in primary forest which had canopy stratum A, B, and C. Canopy cover density in the study area was relatively high with more than 85 % except in burned forest with a canopy cover density of 40.73 %. Macaca nigra more active in location with higher canopy cover density than in areas that have less canopy cover density. The cover was used by Macaca nigra for concealment, sleeping, eating, and resting. Five water resources were recorded in Tangkoko Nature Reserve during this study. Macaca nigra used these water resources as drink water sources, bathing places, reducing heat, and as playground areas. Nineteenth plant species were recorded eaten by Macaca nigra and fruit was their primary diet. During the study, nine groups of Macaca nigra were found. Four group (Rambo I, Rambo II, Rambo III, and Pantai Batu) intensively observed to know its population structure, sex ratio, and population size. Population size of these groups was 221 individuals. Based on the analysis with ArcView 3.3, the study site covering an area of 686.456 ha. Group size was 14 – 72 individuals/group. The group density was 0.58 groups/km2 and population density was 32.2 ind/km2. Ecotourism activities, hunting and habitat pressure (forest fire and Ilegal logging) are the main threats for Macaca nigra sustainability.
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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