Judul: Hubungan antara Titik Panas dengan Perubahan Penutupan/ Penggunaan Lahan (Studi Kasus: Kabupaten Kapuas, Provinsi Kalimantan Tengah)
Abstrak: Land and forest fires in Indonesia have become a serious problem since 1982/1983. Annually the area that was burned widespread, which is caused by the increasing number of forest converted to plantations, agriculture, settlements, and so forth. Indicator commonly used for early detection of forest and land is hotspots, obtained from a meteorology satellite remote sensing. The objectives of this research are to analyze the distribution pattern of hotspots in Kapuas District, analyzed land cover/use change in Kapuas District, analyzed the relationship between hotspots and land cover/use change in Kapuas District, and analyzed the relationship hotspots and rainfall anomaly in Kapuas District. The maximum distribution of hotspots during 2005 to 2011 was indicated in 2006 and 2009; it was expected because of the effect of long term dryness (El-Nino), so the intensity of the fire increased. During 2005 to 2011, many hotspots identified in shrub swamps, shrub, plantations, secondary swamp forest, and secondary upland forest. Hotspots are identified on the site, possibly due to a fire caused by the preparation of agriculture or plantation. It is marked by an increase in land cover change to plantation up to 49.5%, while the secondary swamp forest and secondary upland forest decreased up to 19.2% and 7.83%. The rainfall anomaly is also expected to be able to affect the number of hotspots. When rainfall in the condition of subnormal, then hotspot identified more than when rainfall was above normal.
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Judul: Analisis Pola Sebaran Titik Panas dan Pemodelan Spasial Kerentanan Kebakaran Hutan dan Lahan di Kabupaten Kapuas, Kalimantan Tengah
Abstrak: Land and forest fires in Indonesia become a serious problem since 1982/1983. Kapuas District is one of districts in Central Kalimantan which often experiences land and forest fire. Aim of this research is to analyze the distribution of hotspot in Kapuas District, to identify the driver factors which cause land and forest fire, to build spatial modeling and to map land and forest fire vulnerability index in that area. A Composite Mapping Analysis (CMA) method was used to build spatial modeling. The maximum distribution of hotspot during 2005 to 2012 occured in 2006 and 2009, and it was estimated due to the influence of El-Nino, so the intensity of the land and forest fire increased. Dominant driving factors of land and forest fire vulnerability from the highest to the lowest are depth of peat, distance to central village, land use/cover, road distance, and river distance. Depth of peat is a factor that has a higher influence than others factors. The best spatial modeling for vulnerability of land and forest fire is a model with 5 factors, those are depth of peat, distance to central village, land use/cover, road distance, and river distance. The result of land and forest fire vulnerability mapping showed that high to the very high vulnerability index of the Mantangai sub-district in very large region.
Keyword: land and forest fires vulnerability index, land use/cover, CMA, land and forest fires, hotspot

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