Judul: Pendugaan Jumlah Emisi Karbon sebagai Gas Rumah Kaca (GRK) Akibat Kebakaran Hutan di Kawasan Taman Nasional Gunung Merbabu
Abstrak: Kebakaran Hutan dan Lahan di Indonesia, hampir 99 persen kebakaran hutan di Indonesia disebabkan karena ulah manusia. Taman Nasional Gunung Merbabu (TNGMb) merupakan salah satu taman nasional yang telah mengalami kebakaran hutan di sebagian besar kawasannya. Tujuan penelitian ini untuk menduga jumlah emisi karbon sebagai gas rumah kaca akibat kebakaran hutan di kawasan TNGMb. Penelitian ini menggunakan metode persamaan allometrik untuk menghitung potensi karbon dan metode interpretasi visual untuk menganalisis perubahan lahan TMGMb pada tahun 2015-2016. Hasil perhitungan potensi jumlah emisi karbon akibat kebakaran hutan di kawasan TNGMb sebesar 37363.30 tC, dengan masing-masing senyawa gas rumah kaca menyumbang jumlah emisi yang berbeda-beda yaitu CO2 sebesar 136998.78 tC, CH4 sebesar 597.81 tC, dan N2O sebesar 4.10 tC. Gas CO2 memberikan kontribusi emisi paling besar karena TNGMb didominasi hutan alam yang luas terbakarnya lebih besar dibandingkan dengan tutupan lahan yang lain, selain itu hutan alam memiliki kerapatan jenis pohon tinggi yang banyak menyimpan karbon sedangkan jumlah emisi N2O memberikan kontribusi yang rendah karena kebakaran hutan bukan merupakan sumber utama melainkan dari sektor industri dan transportasi.
Keyword: emisi, kebakaran hutan, gas ruma kaca

Judul: Pendugaan Emisi Gas Rumah Kaca (CO2) Akibat Kebakaran Hutan dan Lahan pada Berbagai Tipe Tutupan Lahan di Provinsi Riau Tahun 2000-2009
Abstrak: Kebakaran hutan dan lahan telah menyebabkan dampak bagi lingkungan dan masyarakat, baik langsung maupun tidak langsung. Kebakaran hutan dan lahan di Indonesia seringkali dikaitkan dengan kegiatan konversi hutan ataupun lahan menggunakan pembakaran. Kegiatan konversi ini didorong karena semakin meningkatnya kebutuhan masyarakat akan suatu lahan. Dampak yang ditimbulkan dari kebakaran hutan dan lahan salah satunya berkaitan dengan emisi karbon yang dihasilkan. Tujuan dari penelitian ini adalah melakukan pendugaan luas area kebakaran hutan dan lahan pada setiap penutupan lahan dan menghitung emisi karbondioksida (CO2) yang dihasilkannya.
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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