Judul: Mapping Soil Biodiversity Around Mining Areas in  Nanggung District Bogor Regency
Abstrak: Area sekitar lahan pertambangan sering mendapatkan dampak positif dan negatif aktivitas pertambangan. Salah satu dampak negatif dari aktivitas pertambangan adalah turunnya keragaman biodiversitas tanah, serta pengaruh aktivitas tambang terhadap kandungan nutrisi dan kehidupan organisme tanah. Sifat biologi tanah seperti keberadaan dan aktivitas mikroorganisme sangat penting untuk proses dekomposisi bahan organik dan mempengaruhi sifat kimia tanah seperti pH, C-organik dan N-total yang menentukan kesuburan tanah dan kemampuan tanah untuk mendukung pertumbuhan tanaman. Penelitian ini bertujuan untuk memetakan biodiversitas tanah dari karakteristik sifat biologi dan kimia tanah di Kecamatan Nanggung, Kabupaten Bogor. Penelitian dilakukan menggunakan metode eksploratif yaitu menggali lebih dalam interaksi kompleks antara sifat-sifat kimia dan biologi tanah dengan biodiversitas tanah di sekitar area pertambangan. Tahapan penelitian ini yaitu pembuatan peta kerja, pengambilan sampel tanah, analisis sifat biologi tanah, analisis sifat kimia tanah, dan analisis data. Pengambilan sampel tanah dilakukan di 7 Satuan Peta Tanah (SPT) dan 27 titik sampel yang terdapat pada zona buffer dengan radius 500 meter, 1000 meter, dan 2000 meter dari lahan pertambangan. Lokasi penelitian terletak di 2 Desa yaitu Bantarkaret dan Cisarua, Kecamatan Nanggung, Kabupaten Bogor. Hasil analisis menunjukkan keanekaragaman fauna tanah berkisar antara rendah hingga sedang dengan 418 individu dan 6 Ordo yaitu Oligochaeta, Hymenoptera, Isoptera, Anthuroidea, Orthoptera, dan Coleoptera. Semua lokasi memiliki keanekaragaman fauna tanah dengan katergori rendah kecuali pada lokasi Sawah (7.3) dan Hutan (15.6) yang memiliki keanekaragaman kategori sedang. Total mikrob dan fungi tertinggi terdapat pada buffer 2000 meter dari area pertambangan. Total mikrob tertinggi terdapat pada area tambang sedangkan fungi tertinggi terdapat pada sawah. Sifat kimia dengan buffer ideal terdapat pada buffer 1000 meter dari area pertambangan, sedangkan sifat kimia dengan penggunaan ideal terdapat pada penggunaan lahan pemukiman desa.
Keyword: Penggunaan lahan, fauna tanah, sifat biologi tanah, sifat kimia tanah, zona buffer

Judul: Analysis Biological and Chemical Characteristics of Soil in Several Types Land Use in Bogor Regency
Abstrak: Penggunaan lahan di Kabupaten Bogor memiliki vegetasi beragam dan karakteristik tanah berbeda. Karakteristik tanah memiliki pengaruh terhadap pengelolaan lahan. Keberadaan mikrob tanah dapat digunakan sebagai bioindikator kesuburan tanah. Kesuburan tanah dihasilkan oleh interaksi sifat fisik, kimia, dan biologi tanah. Penelitian ini bertujuan mengetahui karakteristik sifat biologi dan kimia tanah pada berbagai tipe penggunaan lahan di Kabupaten Bogor. Pengambilan sampel tanah dilakukan dengan menggunakan metode purposive random sampling dengan 5 kali ulangan. Lokasi penelitian meliputi delapan penggunaan lahan yaitu kebun bambu, kebun jabon, kebun sengon, kebun lamtorogung, tegalan, tanaman sereh wangi, kebun buah naga, dan kebun pisang. Hasil analisis menunjukkan lahan pisang dan buah naga memiliki kadar C-organik, N-total tertinggi dan populasi fungi tertinggi yaitu 3,78 x 105 CFU/gram. Sedangkan pada lahan di Kecamatan Jasinga yang memiliki hara dan populasi fungi yang cukup dan populasi Azotobacter tertinggi yaitu pada lahan lamtorogung dapat diusahakan untuk tanaman perkebunan yang lebih memiliki nilai jual lebih tinggi seperti pada lahan tegalan dapat ditanami pohon karet, kelapa sawit, atau kakao., Land use in Bogor Regency has diverse vegetation and different soil characteristics. Soil characteristics have an influence on land management. The presence of soil microbes can be used as bioindicators of soil fertility. Soil fertility is produced by the interaction of physical, chemical, and biological properties of the soil. This study aims to determine the characteristics of the biological and chemical properties of soil in various types of land use in Bogor Regency. Soil sampling using the purposive random sampling method with 5 repeats. The research location includes eight land uses, namely bamboo, jabon, sengon, lamtorogung gardens, moorings, fragrant lemongrass plants, dragon fruit orchards, and banana gardens. The results of the analysis showed that banana and dragon fruit fields had the highest C-organic, N-total levels and the highest fungi population of 3,78 x 105 CFU/gram. Meanwhile, on land in Jasinga District which has sufficient nutrients and fungi populations and the highest Azotobacter population, namely on lamtorogung land, it can be cultivated for plantation crops that have a higher selling value, such as in moorlands that can be planted with rubber trees, oil palm, or cocoa.
Keyword: Azotobacter sp., microbes, soil fertility, total fungi

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