Judul: Model Penduga Kandungan Karbon Pada Pohon Kelompok Jenis Rhizopora spp. dan Bruguiera spp. Dalam Tegakan Hutan Mangrove (Studi Kasus di Indragiri Hilir Riau)
Abstrak: Hutan Mangrove merupakan suatu tipe hutan yang dipengaruhi oleh pasang surut air laut, tekstur tanah dan salinitas air laut. Jenis-jenis pohon mangrove diduga memiliki kandungan karbon yang tinggi yang dapat menjadi sumber emisi karbon yang akan mencemari lingkungan.
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Judul: Biomassa Dan Serapan Karbon (Carbon Sequestration) Pada Tegakan, Nekromassa Kayu Dan Substrat Mangrove Di Bulaksetra, Pangandaran
Abstrak: Mangrove mampu menyerap CO2 atmosfer melalui mekanisme fotosintesis, sehingga dapat berperan dalam mitigasi perubahan iklim yang hasilnya disimpan pada akar, batang, daun, serta tegakan mati (nekromasa) dan substratnya. Penelitian ini bertujuan untuk mengkaji penyerapan karbon oleh mangrove Rhizophora mucronata. Pengukuran dan sampling dilakukan pada November 2014 dan Maret 2015 di Bulaksetra pada 2 plot pengamatan: dekat sungai dan dekat pantai. Metode pengukuran biomassa dan karbon tersimpan mengacu pada Hairiah dan Rahayu (2007), sedangkan analisis C-organik dengan metode Walkey and Black. Hasil penelitian menunjukkan bahwa parameter fisik di lokasi penelitian mempengaruhi biomassa dan serap karbon mangrove. Total biomassa karbon pada tegakan, akar, nekromasa kayu dan serasah adalah 3.335 g C/m2, 334 g C/m2, 1.344 g C/m2 dan 260 g C/m2. Kandungan karbon di substrat mangrove pada kedalaman 1-30 cm, 31-70 cm dan 71-100 cm adalah 13,34 g C/m2, 3,94 g C/m2,dan 1,58 g C/m2; dengan jenis struktur sedimen lempung berpasir, pasir berlempung dan pasir. Hasil pengukuran serapan karbon di substrat mangrove sangat rendah dibandingkan dengan hasil kajian sebelumnya di lokasi lain..Hal ini diakibatkan oleh spesies mangrove yang diukur bukanlah asli tumbuh di Bulaksetra, melainkan hasil rehabilitasi yang baru berusia 7 tahun sejak penanaman.
Keyword: Bulaksetra, CO2, mangrove, serapan karbon, substrat, tegakan

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