Judul: Potensi Pemanfaatan Limbah Biji Karet (Hevea brasiliansis Muell.Arg) Sebagai Sumber Energi Alternatif Biokerosin Untuk Keperluan Rumah Tangga (Studi Kasus eli Desa Nanga Jetak Kec.Dedai Kab. Sintang ProY.Kalimantan Barat)
Abstrak: Pohon karet selain menghasilkan getah, juga menghasilkan biji yang dapat dimanfaatkan sebagai sumber benih. Terjadinya krisis energi dan menipisnya BBM dari fosil berdampak pada Iingkungan antara lain berkontribusi terhadap pemanasan global dengan melepaskan gas rumah kaca (karbondioksida) ke atmosfrr dan juga merupakan sumber polusi udara. Naiknya harga Bahan Bakar Minyak (SBM) menyebahkan kelangkaan minyak seperti minyak tanah, premium dan solar.
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Judul: Studi Potensi Biomassa Dan Massa Karbon Pohon Karet (Hevea Brasiliensis Muell Arg) Di Hutan Karet Rakyat Desa Bungku, Provinsi Jambi
Abstrak: Gagasan perdagangan karbon dapat diterapkan pada usaha hutan rakyat. Tegakan karet (Hevea brasiliensis Muell Arg) pada hutan karet rakyat sebagai penghasil lateks dapat dimanfaatkan sebagai pengikat karbon dioksida dan diubah ke dalam bentuk biomassa. Penelitian ini bertujuan untuk mengetahui potensi biomassa dan massa karbon hutan karet rakyat Desa Bungku, Provinsi Jambi. Potensi biomassa dan massa karbon dihitung dengan menggunakan persamaan alometrik lokal yaitu W= 0.2661D2.1438 dengan R2 = 0.968 dan C= 0.0661D2.1863 dengan R2 = 0.892. Penelitian menghasilkan potensi biomassa sebesar 45.538 ton/ha dan massa karbon sebesar 12.868 ton/ha. Potensi biomassa dan massa karbon berturut-turut pada kelas umur 1 sebesar 15.198 ton/ha dan 4.215 ton/ha, pada kelas umur 2 sebesar 41.971 ton/ha dan 11.882 ton/ha, pada kelas umur 3 sebesar 53.628 ton/ha dan 15.168 ton/ha, pada kelas umur 4 sebesar 51.959 ton/ha dan 14.764 ton/ha, pada kelas umur 5 sebesar 77.682 ton/ha dan 22.129 ton/ha dan pada kelas umur 6 sebesar 85.144 ton/ha dan 24.410 ton/ha.
Keyword: pohon karet, biomassa, massa karbon

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