Judul: Spatial Modelling on Susceptibility of Fires in Peatland, a Case Study in District of Bengkalis, Riau Province
Abstrak: Peatland is one of ecosystem types in Indonesia which plays important role in maintaning terrestrial carbons, hydrological cycles and preserving biodiversity. Recently, most of the peatland (forest and non forest) has been degraded. One of the causes of degradation is fire. This study was conducted to establish a model and map of peat fire risk in District of Bengkalis, Riau Province. The model was established by hotspot distribution as an indicator of fire occurrences. Environmental and infrastructure aspects were employed as supportive factors to fire. Factors which were used to describe the environmental aspect were landcover and vegetation types, peat depth, and greenness level of vegetation. Factors of infrastructure aspect were the distance of fire occurrences with road network, distance to the river network and to the center of villages. Weighting and scoring were the basis of model development using the method of Complete Mapping Analysis (CMA). The model suggested that peat fire is more strongly affected by infrastructure aspect when compared with environmental aspect with the weight of 0.658 and 0.342 respectively. This explains that fires occurred were triggered more by human factor rather than environmental factor. The chosen factor of infrastructure aspect is distance from occurenced fires of road network. The selected environmental aspects were landcover and vegetation types, peat depth and greenness level of vegetation. Seemingly, the distance of river and center of villages may not be the supportive factors of peat fires, as showed by the presence of hotspots in which found mostly in the area far from the river and village center. The model can explain the condition of average of fire occurrences with the validation of 80%. Therefore, it can be used as the referrence in developing and establishing of polic ies for peat fire prevention and suppression.
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Judul: Variabilitas Iklim dan Kejadian Kebakaran Hutan dan Lahan Gambut di Kabupaten Bengkalis, Provinsi Riau.
Abstrak: Bengkalis is one of districts which is vulnerable to forest and peatland fires in Riau due to land use change. This research aims to compare the observation climate data of Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG) and global climate data from Climatic Research Unit (CRU) satellite as factors that affect the incidence of forest and peatland fires, analyze the distribution of forest and peatland fires in Bengkalis, and analyze the correlation between the elements climate (rainfall, maximum air temperature, and average air temperature) of CRU satellite and hotspot as an indicator of forest and peatland fires in Bengkalis. The result of the research revealed that climate patterns of CRU satellite in line with climate patterns of BMKG observations.. Spatial distribution of hotspots, 1998, 2002, 2004, 2005, 2006, and 2009 had the highest number of hotspots. Most hotspots are in the peat soil types Hemists/Saprists (60/40), very deep and on land cover shrub swamp. The maximum air temperature and average air temperature significant (p-value <0.05) to the number of hotspots while the rainfall did not significantly affect the number of hotspots. The best model also showed the absence of a suitable model to describe the relationship between elements of the climate and the number of hotspots.
Keyword: Climatic Research Unit, hotspot, climate, peat fires

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