Judul: Biaya Kebakaran Gambut yang ditanggung oleh Rumah Tangga Petani di Kecamatan Air Sugihan
Abstrak: Peatlands in OKI Regency are the largest peatlands in South Sumatra which have an area of 638,379 hectares of peatland with 65 percent being hydrological peat areas. Peatlands have many benefits and have economic value if they can be managed properly. However, the uniqueness of peatlands is threatened by land degradation which is accompanied with development of plantations, agriculture, and industry. Currently, forest and peatland fires are the main focus given the enormous impact they have on the lives. Research purpose was to measure cost incurred by farming families due to forest and peatland fire, the research location was carried out in Air Sugihan District, and 3 villages were taken representing the sub-district, namely Rengas Abang Village, Nusar Karta Village, and Jadi Mulya Village. Fore costs are measured through Cost of Illness, Loss of Earings, Equivalency Analysis, and Losses on Farming. The total cost borne by farmer households in Air Sugihan District due to peatland fire is predicted to be Rp. 773.338.080 with a percentage distribution of 1,91 % medical expenses, 0,27 % additional drinking water purchase cost, 41,91 % additional farming cost, and 55,91 % loss of farmer income.
Keyword: Cost, Fire Impact, Peat Fire, Farmers Perception

Judul: Estimating the Severity of Peat Fires in Siak Regency, Riau Province
Abstrak: Kebakaran yang terjadi pada lahan gambut memiliki dampak yang sangat buruk bagi kesehatan manusia dan bagi lingkungan. Dampak yang ditimbulkan dari kebakaran tersebut perlu dianalisis lebih lanjut. Salah satu pendekatan dalam penilaian dampak kebakaran adalah pendekatan tingkat keparahan kebakaran (fire severity) yang menilai dampak kebakaran terhadap vegetasi dan sifat-sifat tanah. Perkembangan teknologi penginderaan jauh terkait penilaian tingkat keparahan kebakaran telah banyak berkembang, salah satunya dengan metode Normalized Burn Ratio (NBR). Namun, penerapan metode tersebut perlu diverifikasi dengan pengukuran di lapangan (ground check) sehingga diharapkan informasi yang diperoleh lebih akurat dan efektif dalam upaya penilaian dampak kebakaran hutan dan lahan. Penelitian ini berfokus pada estimasi tingkat keparahan kebakaran lahan gambut di Kabupaten Siak, Provinsi Riau agar terciptanya langkah-langkah dalam upaya penanganan pascakebakaran, baik itu upaya rehabilitasi, evaluasi, dan penegakan hukum. Lokasi penelitian dilakukan pada lahan gambut terbakar di Kabupaten Siak, Provinsi Riau. Pengolahan data analisis citra dengan metode Normalized Burning Ratio (NBR) dilakukan pada bulan Mei hingga bulan Agustus 2021 di Laboratorium Kebakaran Hutan dan Lahan, Fakultas Kehutanan dan Lingkungan IPB. Hasil dari perhitungan metode Normalized Burning Ratio digunakan sebagai penentuan lokasi pengambilan data sampel di lapangan. Pengambilan dan pengukuran sampel di lapangan dengan metode fire severity, dilakukan pada bulan September tahun 2021. Berdasarkan hasil perhitungan Normalized Burning Ratio, Kabupaten Siak memiliki luasan tingkat keparahan kebakaran tinggi sebesar 93,33 ha, tingkat keparahan sedang hingga tinggi seluas 6.081 ha, tingkat keparahan sedang hingga rendah seluas 11.467 ha, dan tingkat keparahan rendah seluas 34.724 ha. Jika dibandingkan dengan hasil pengukuran di lapangan (ground check), metode fire severity menunjukkan hasil tingkat keparahan yang sama dengan metode Normalized Burning Ratio pada lahan dengan tingkat keparahan kebakaran tinggi., Fires that occur on peatlands have a very bad impact on human health and the environment. The impact that incurred because of these fires needs to be analyzed further. One of the approaches in assessing the impact of fires is the fire severity approach, which assesses the impact of fires on vegetation and soil properties. Remote sensing technology related to the assessment of fire severity has been broadly developed, one of the methods is the Normalized Burn Ratio (NBR). However, the application of this method needs to be verified with measurements in the field (ground check) in order to obtain more accuracy and effectiveness in assessing the impact of forest and land fires. This research focuses on estimating the severity of peatland fires in Siak Regency, Riau Province in order to create steps in post-fire management efforts, including rehabilitation, evaluation, and law enforcement efforts. The research location was conducted on a burning peat area in Siak Regency, Riau Province. Image analysis data processing using the Normalized Burning Ratio (NBR) method was carried out from May to August 2021 at the Forest and Land Fire Laboratory, Faculty of Forestry and Environment, IPB University. The results of the calculation of the Normalized Burning Ratio method are used to determine the location of field sampling. Data collection and measurements in the field sampling using the fire severity method was carried out in September 2021. Based on the results of the Normalized Burning Ratio
Keyword: Fire Severity, NBR, Peatland Fire, Remote Sensing

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