Judul: Etnobotani Masyarakat Tengger: Studi Kasus Desa Ngadas Wilayah Enclave Taman Nasional Bromo Tengger Semeru
Abstrak: Kawasan pelestarian alam di Jawa Timur salah satunya yaitu Taman Nasional Bromo Tengger Semeru (TNBTS). Penelitian bertujuan untuk melakukan studi etnobotani dalam rangka pelestarisan pengetahuan. Prosedur penelitian meliputi studi literatur, observasi lapang, wawancara, analisis vegetasi, dan analisis data. Hasil penelitian menunjukkan pemanfaatan tumbuhan oleh masyarakat Tengger Desa Ngadas sebanyak 69 jenis tumbuhan. Kelompok penggunaan terdiri dari tumbuhan pangan dan sayuran, tumbuhan obat, tumbuhan adat dan keagamaan, tumbuhan untuk kayu bakar, bahan bangunan, konservasi, dan pakan ternak. Indeks Nilai Penting tertinggi plot ladang untuk tingkat pertumbuhan pohon dimiliki oleh cemara gunung sebesar 229,56% pada plot hutan untuk tingkat pertumbuhan pohon dimiliki oleh kayu manisan sebesar 39,95%. Nilai Index of Cultural Significance tertinggi dimiliki oleh padi yang berasal dari luar Tengger sebesar 72. Indeks Nilai Penting cemara gunung di ladang sebesar 229,56% dan nilai Index of Cultural Significance sebesar 45. Hal ini menunjukkan pelestarian cemara gunung tinggi jika dilihat dari nilai INP meskipun pemanfaatan cemara gunung tergolong ke dalam kategori sangat tinggi.
Keyword: Ethnobotany, Ngadas, Tengger

Judul: Etnobiologi masyarakat tengger di Bromo Tengger Semeru Jawa Timur
Abstrak: This ethnobiological research focused on the ethnoecological, ethnobotanical, and ethnozoological study of the adaptation process (correlating to management concepts, impact on people’s activities, and technology usage) of the Tengger society in Bromo Tengger Semeru, East Java to environmental conditions where they were actively using and managing natural resources. The goals of this research were to study the beliefs, knowledge, and practice of Tengger society for the comprehensive understanding of landscape use and management, and to reveal the indigenous knowledge of Tengger society in managing their natural resources (plants and animals) which included species diversity, the index of ecological important value (INP), and the index of cultural significance (ICS). The research data consisted of ecological, ethnological, ethnobotanical and ethnozoological data. Ecological data was collected using vagetation analysis, while the rest of the data was collected using the participatory ethnobotanical appraisal, structured and open ended interviews, and direct observation. The Tengger society arranged their areas based on their function and usefulness including area of housing, agriculture, conservation, ecotourism, and sacral. Traditional ecological knowledge applied for environmental conservation consisted of an agricultural system that implement terasiring combined with plant borders, stall locations separated from houses, and planting Casuarina tree arranged by traditions. Tengger people depend on plant resources for their livelihood, and they have good knowledge on plant diversity surrounding them. The various plant utilization by Tengger society include food (75 species); medicines (121 species); construction, firewood and local technology (53 species); cosmetics, handycraft, cigarette, colors (40 species); forage (44 species); ornamental plants (140 species); fruit (49 species); and ritual (94 species). Calculations of the index of cultural significance showed that rice has a very high value and ten other plant species have high value in Tengger culture. For Tengger people, various animals have an economic value, and can be used for food, ritual, transportation, and objects for tourism.The indigenous knowledge on wild animals and their uses were very good. Tengger people distinguished 120 species consisting of 64 species of Aves, 32 species of Mammals, 9 species of Reptilia, 3 species of Diptera, 2 species of Decapoda, 1 species of Arachnidae, 1 species of Orthoptera, 1 species of Hypnoptera and 6 species of Pisces.
Keyword: 

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