Judul: Pendugaan Emisi Gas Rumah Kaca CO2 Akibat Kebakaran Hutan dan Lahan pada Berbagai Tipe Tutupan Lahan di Provinsi Kalimantan Barat Tahun 2000-2009
Abstrak: Forest fire is one of forest disturbance that may reduce forest area in Indonesia. West Kalimantan is one of Indonesian provinces that are vulnerable to forest fire. It is caused by land use changes that occurred in West Kalimantan Province. Forest fire has a huge impact, it has related with carbondioxide (CO2) emissions are produced. This research was conducted between April and June 2012 at Forest Fires Laboratory, Departement of Silviculture, Faculty of Forestry, Bogor Agricultural University. The data used is hotspot data from Fire Information Resources Management System (FIRMS), precipitation data from Center for Meteorology Climatology and Geophysic (BMKG), and land cover map of West Kalimantan from Roundtable on Sustainable Palm Oil (RSPO). Hotspot data were processed to estimate burned area in each land covers, thus carbondioxide emissions could be obtained. The purpose of this research is to analyze forest and land fire occurrences on different land covers in West Kalimantan in 2000-2009 and to estimate their carbon dioxide emissions., Kebakaran hutan merupakan salah satu gangguan yang semakin menurunkan luas hutan di Indonesia. Kalimantan Barat merupakan salah satu provinsi dengan tingkat kebakaran hutan dan lahan yang tinggi di Indonesia. Hal tersebut didorong oleh perubahan penggunaan lahan yang terjadi di Provinsi Kalimantan Barat. Kebakaran hutan dan lahan memiliki dampak yang sangat besar, salah satunya terkait emisi karbondioksida yang dihasilkan. Penelitian ini bertujuan untuk menganalisis kejadian kebakaran hutan dan lahan pada berbagai tipe tutupan lahan di Kalimantan Barat dan menduga luas area terbakar dari titik panas yang terdeteksi di Kalimantan Barat pada tahun 2000-2009 serta menduga emisi karbondioksida yang dihasilkannya. Penelitian ini dilakukan pada bulan April sampai dengan Juni 2012 di Laboratorium Kebakaran Hutan, Departemen Silvikultur, Fakultas Kehutanan, Institut Pertanian Bogor dengan menggunakan data hotspot (titik panas) harian, data curah hujan, dan peta tutupan lahan Provinsi Kalimantan Barat. Data sebaran hotspot tersebut diolah untuk memperoleh pendugaan potensi luas area terbakar. Luas area terbakar pada masing-masing penutupan lahan digunakan untuk menduga emisi karbondioksida akibat kebakaran hutan dan lahan.
Keyword: 

Judul: Pendugaan Emisi CO2 sebagai Gas Rumah Kaca Akibat Kebakaran Hutan dan Lahan pada Berbagai Tipe Penutupan Lahan di Provinsi Kalimantan Tengah Tahun 2000-2009
Abstrak: Forest fires became yearly severe problems in Indonesia. Central Kalimantan is one of Indonesia’s provinces that experienced severe forest fires in the last decade. Land conversion may be objected as one of trigger of forest fires that will produce carbon dioxide emissions. The purpose of this research is to calculate green house gas emission especially CO2 using hotspot data based on land use conversion at Central Kalimantan in the period 2000-2009. This research conducted between April and June 2012 at Forest Fires Laboratory, Silviculture Department, Faculty of Forestry, Bogor Agricultural University. The data used are MODIS hotspot data from Fire Information Resources Management System (FIRMS), precipitation data from Indonesian Meteorological, Climatological and Geophysical Agency (BMKG), and land cover map of Central Kalimantan from Roundtable on Sustainable Palm Oil (RSPO). The research results showed that primary forest decreased by 10% in 2009 while palm oil plantation increased more than 190% in 2009 compared to 2005. The numbers of hotspots are increasing from 2000 to 2009. The highest number of hotspots occurred in 2006 as 22,505 hotspot. The number of hotspots occurred on peat land (7,180) is found more than on mineral land (6,391). The increasing burned area from 2000 to 2009 correlates with the increasing of hotspots number on each land covers. Swamp shrubs have the highest burnt area of 113,609 ha, while the highest carbon emission is found from the land cover conversion of swamp shrubs into palm oil plantations as 801,764 tons. The highest carbon dioxide emission on mineral land in Central Kalimantan is found from shrubs in 2005 (459,427 ton). The highest carbon dioxide emission on peat land is found at disturbed swamp forest in 2005 (80,851 ton) and undisturbed swamp forest in 2009 (180,531 ton).
Keyword: 

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