Judul: Perbandingan Keanekaragaman Jenis Burung Antara Lahan Pasca Terbakar dan Tidak Terbakar di Perkebunan Sawit PT. Waimusi Agroindah, Sumatera Selatan
Abstrak: Banyak anggapan bahwa kebakaran hutan dan lahan dapat mengakibatkan dampak yang merugikan bagi keanekaragaman hayati terutama dampak negatif terhadap tumbuhan dan satwaliar. Oleh sebab itu perlu dilakukan penelitian mengenai perbandingan keanekaragaman jenis burung dan tumbuhan bawah yang ada di lahan pasca terbakar, lahan tidak terbakar dan perkebunan kelapa sawit. Penelitian dilakukan pada Februari hingga Maret 2017. Pengambilan data keanekaragaman burung menggunakan metode transek dan analisis vegetasi menggunakan metode jalur berpetak dengan luas 20m x 20m dan pengambilan plot contoh ukuran 2m x 2m sebanyak 5 plot. Analisis data yang digunakan adalah Indeks Kemerataan (E), Indeks Kekayaan (Dmg) dan Indeks Kesamaan komunitas (IS). Hasil pengamatan menunjukkan bahwa pada lokasi lahan pasca terbakar terdapat 36 jenis burung dan 16 jenis tumbuhan bawah, pada lokasi lahan tidak terbakar terdapat 25 jenis burung dan 13 jenis tumbuhan bawah serta pada lokasi lahan kelapa sawit terdapat 24 jenis burung dan 17 jenis tumbuhan bawah. Kekayaan jenis burung tertinggi terdapat di lahan pasca terbakar yaitu dengan nilai Dmg=5.44, sedangkan kemerataan tertinggi terdapat di lahan tidak terbakar sebesar E=0.85.
Keyword: burung, kebakaran, keanekaragaman, kemerataan, kelapa sawit

Judul: Perbandingan Keanekaragaman Jenis Mamalia antara Lahan Pasca Terbakar dan Tidak Terbakar di Perkebunan Sawit PT Waimusi Agroindah, Sumatera Selatan
Abstrak: Perkebunan kelapa sawit berkontribusi terhadap pertumbuhan dan perkembangan ekonomi. Namun kebakaran di Perkebunan kelapa sawit merupakan ancaman bagi lingkungan dan masyarakat khususnya keanekaragaman mamalia. Penelitian ini bertujuan untuk menghitung keanekaragaman jenis mamalia dan kesamaan komunitas mamalia antara lahan pasca terbakar, lahan tidak terbakar dan lahan kelapa sawit. Penelitian dilaksanakan pada bulan Februari sampai Maret 2017. Inventarisasi mamalia dilakukan dengan metode strip transect, perangkap, dan pengamatan jejak serta dianalisis menggunakan indeks kekayaan jenis, indeks kemerataan jenis dan kesamaan komunitas. Hasil penelitian menunjukkan pada lahan pasca terbakar memiliki jumlah jenis lebih banyak (5 jenis dan 4 famili) dan kekayaan jenis mamalia lebih tinggi (0.1481) dibandingkan lahan tidak terbakar (3 jenis, 3 famili dan 0.1429). Namun apabila dibandingkan dengan lahan kelapa sawit ditemukan jenis mamalia yang lebih banyak (6 jenis dan 6 famili) dan kekayaan jenis tertinggi (0.2632). Indeks kesamaan komunitas mamalia antar lahan berkisar 0.5000 sampai 0.6667. Terdapat 3 jenis mamalia yang dilindungi menurut PP No 7 Tahun 1999.
Keyword: lahan pasca terbakar, mamalia, perkebunan sawi

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