Judul: Fisiologi dan Karakteristik Daun Rhizophora mucronata Lam. pada berbagai Umur Tanaman dengan Teknik Penanaman Guludan di Angke Kapuk Jakarta
Abstrak: Rehabilitasi kawasan mangrove dengan teknik guludan perlu diukur tingkat keberhasilannya. Salah satu indikator yang mudah dilakukan untuk mengukur keberhasilan pertumbuhan yaitu fisiologi dan karakteristik daun. Tujuan penelitian ini adalah menduga kandungan klorofil dan stomata daun serta mendeskripsikan karakteristik daun tanaman Rhizophora mucronata pada umur berbeda. Pengukuran dilakukan di Kawasan Rehabilitasi Mangrove di Angke Kapuk Jakarta. Pengukuran klorofil dilakukan di lapang dengan alat chlorophyll-meter, sampel stomata diambil dengan metode replika, serta karakteristik daun mengacu pada Standardized Protocol for the Measurement of Leaf Traits. Hasil penelitian menunjukkan bahwa tanaman R. mucronata berumur 1, 6, dan 10 tahun tidak berbeda signifikan dalam kandungan klorofil, kerapatan stomata dan karakteristik luas daun. Adapun karakteristik daun (Specific Leaf Area dan Leaf Dry Matter Content) menunjukkan perbedaan yang signifikan di antara umur tanaman yang berbeda. Walaupun demikian, dalam hal ini kandungan klorofil tertinggi 73.58 μg/cm2 pada umur tanam 1 tahun dan kerapatan stomata 47-58 stomata/mm2 di epidermis bawah daun. Kondisi lingkungan berupa suhu udara dan kandungan unsur hara tanah mengalami perubahan seiring pertambahan umur tanaman.
Keyword: fisiologi daun, karakteristik daun, Rhizophora mucronata, teknik guludan

Judul: Laju Dekomposisi Serasah Daun Rhizophora mucronata pada Beberapa Umur Tegakan yang Ditanam dengan Teknik Guludan
Abstrak: Dekomposisi serasah merupakan komponen penting dari proses siklus nutrisi yang memberikan kontribusi besar terhadap kesuburan air dan biota yang hidup di sekitarnya. Tujuan penelitian ini adalah mengukur laju dekomposisi serasah daun Rhizophora mucronata serta kandungan unsur hara Nitrogen yang dilepas selama proses dekomposisi serasah daun R. mucronata di Kawasan Hutan Mangrove Angke Kapuk, Jakarta. Dekomposisi serasah daun mangrove dilakukan menggunakan litter-bag berukuran 30 cm x 40 cm. Setiap litter-bag berisi 100 g berat kering serasah R. mucronata. Litter-bag diletakkan di guludan tahun tanam 2009 (LTG09), guludan tahun tanam 2011 (LTG11), dan guludan tahun tanam 2013 (LTG13). Hasil penelitian selama 12 minggu pengamatan menunjukkan bahwa laju rata-rata dekomposisi serasah daun tanaman R. mucronata berumur 7 dan 11 tahun yang tumbuh pada tanah dengan kandungan BOT berkategori tinggi (BOT > 6%) adalah relatif sama, tetapi relatif lebih tinggi dari laju dekomposisi serasah daun tanaman R. mucronata berumur 9 tahun yang tumbuh pada tanah dengan kandungan BOT berkategori sedang (4.90%). Pada semua umur tanaman, kandungan unsur hara Nitrogen total dua mingguan dari serasah daun R. mucronata selama proses dekomposisi berkategori relatif tinggi (N > 0.5%).
Keyword: guludan, laju dekomposisi serasah, Rhizophora mucronata

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