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: Modeling the Distribution of Fire Prone Areas Using the Maximum  Entropy Model (Case Study of PT Jambi Batanghari Plantation)
Abstrak: Land and forest fires in Indonesia are regional and global disasters that emerge adverse impacts on the surrounding environment and biome. These events are important to be studied further in order to provide information for prevention and management of land and forest fires. One of the information required is the level of fire susceptibility in an area. The method performed in predicting fire-prone areas in this study is maximum entropy. This model can identify the distribution of fire prone areas and determine the most influential parameters. The maximum entropy model input is divided into 2, namely the sample input and the environmental layer input. The fire hazard map is generated from the probability value of fire occurrences in each pixel area. The most influential parameters are land cover, radiation, wind speed, and peat depth, with 33.6%, 17.6%, 14.1%, and 13.4% contribution, respectively. Land cover in areas with high fire susceptibility are shrubs, built-up land, and oil palm plantations. This land cover is located in peatlands with more than 3 meters depth and also mineral soils. This MaxEnt model has an AUC accuracy value of 0.85., Kebakaran hutan dan lahan di Indonesia adalah peristiwa bencana regional dan global yang banyak memberikan dampak negatif bagi lingkungan dan mahluk hidup di dalamnya. Kejadian-kejadian kebakaran di Indonesia ini perlu dikaji, terutama untuk dapat memberikan informasi bagi usaha-usaha, pengelolaan untuk mencegah kebakaran hutan dan lahan. kebakaran hutan dan lahan. Salah satu informasi yang diperlukan adalah mengetahui tingkat kerawanan kebakaran di suatu wilayah. Metode yang digunakan dalam memprediksi wilayah rawan kebakaran pada penelitian ini maximum entropy. Model ini dapat digunakan untuk memetakan distribusi wilayah rawan kebakaran dan menentukan parameter yang paling berpengaruh. Input model maximum entropi terbagi menjadi 2 yaitu input sample dan input environmental layer. Peta kerawanan kebakaran dihasilkan dari nilai probabilitas kejadian kebekaran di setiap pixel area. Parameter yang paling berpengaruh adalah parameter tutupan lahan, radiasi, kecepatan angin, dan kedalaman gambut masing-masing yaitu 33,6%,17,6%, 14,1%, dan 13,4%. Tutupan lahan yang berada pada wilayah dengan kerawanan kebakaran tinggi adalah semak belukar, lahan terbangun, lahan persemaian kelapa sawit, dan perkebunan kelapa sawit. Tutupan lahan ini berada di kawasan gambut dengan kedalaman 3 meter atau lebih dan juga tanah mineral. Model MaxEnt ini memiliki nilai akurasi AUC sebesar 0,85.
Keyword: Maximum Entropy, Hotspot, Environmental Layer, Normalized Burn Ratio, Peat

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