Judul: Hubungan C-Organik dengan Respirasi Tanah dan Kelimpahan Makrofauna Tanah di Beberapa Tegakan Tahura Sultan Thaha Syaifuddin, Jambi.
Abstrak: C-organik tanah menunjukkan kadar bahan organik yang terkandung dalam tanah. Keberadaan C-organik dalam tanah akan mendukung kegiatan mikroorganisme tanah. Aktivitas mikroorganisme tanah dapat diamati melalui respirasi tanah. Penelitian ini bertujuan untuk mengkaji hubungan antara C-organik dengan respirasi tanah dan kelimpahan makrofauna tanah. Penelitian ini dilakukan dengan mengambil sampel tanah pada empat tegakan yang berbeda yaitu hutan sekunder, kebun sawit, tegakan karet, dan tegakan ulin. Masing-masing plot dibuat dengan ukuran 20 cm x 20 cm dan ketebalan 5 cm. Sampel tanah di analisis sifat fisik, sifat kimia, dan laju respirasi tanah. Uji korelasi dilakukan pada C-organik, laju respirasi tanah, dan kelimpahan makrofauna tanah. Hasil penelitian menunjukkan C-organik tertinggi pada hutan sekunder (2.82%), laju respirasi tertinggi di tegakan karet (5.21 mg CO2/100 g tanah/hari), kelimpahan makrofauna tanah tertinggi di kebun sawit dengan 24 individu/plot. C-organik berkorelasi positif dengan kelimpahan makrofauna tanah dan berkorelasi negatif dengan laju respirasi tanah.
Keyword: C-organik, korelasi, laju respirasi tanah, makrofauna tanah

Judul: Hubugan Kapasitas Tukar Kation dengan Respirasi dan Aktivitas Makrofauna Tanah di berbagai tegakan Taman Hutan Raya, Jambi
Abstrak: Respirasi tanah adalah proses pengeluaran (emisi) CO2 dari tanah. Respirasi tanah menunjukkan jumlah CO2 yang dihasilkan oleh dekomposisi bahan organik, respirasi autotrofik (akar tanaman), dan respirasi heterotropik (mikroba dan fauna tanah). Salah satu faktor yang mempengaruhi besarnya respirasi tanah yaitu Kapasitas Tukar Kation (KTK). Penelitian ini betujuan untuk mengkaji hubungan KTK dengan laju respirasi dan aktivitas makrofauna tanah di berbagai lokasi di Taman Hutan Raya Jambi. Pengambilan sampel tanah dilakukan pada empat lokasi, yaitu hutan karet, hutan ulin, hutan sekunder, dan kebun sawit. Pada masing-masing lokasi terdapat tiga ulangan, dan pada setiap ulangan terdapat lima plot. Tiap plot berukuran 20 cm x 20 cm. Sampel tanah yang telah diambil kemudian dianalisis sifat fisik, sifat kimia, respirasi tanah, dan jumlah makrofauna tanahnya. Hasil penelitian menunjukkan bahwa nilai KTK berkorelasi negatif dengan laju respirasi tanah, tetapi berkorelasi positif dengan jumlah makrofauna tanah/plot. Nilai KTK tanah tertinggi terdapat pada kebun sawit sebesar 14.876 (cmol(+)/kg). Laju respirasi tanah terbesar terdapat pada hutan karet sebesar 5.21 mg CO2/100g tanah/hari dan jumlah makrofauna tanah terbanyak terdapat di kebun sawit sebesar 24 individu/plot.
Keyword: KTK, korelasi, makrofauna tanah, respirasi tanah

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