Judul: Penilaian Bahaya Kebakaran Hutan Jati Di Kph Semarang Berdasarkan Indeks Kekeringan Keetch Byram ( Keetch Byram Drought Indexlkbdi)
Abstrak: Keetch·Byram Drought Index merupakan salah satu metode penilaian bahaya kebakaran hutan yang dinilai dapat diandalkan untuk menilai tingkat kerawanan suatu daerah terhadap bahaya kebakaran hutan. Sistem penilaian babaya kebakaran ini didasarkan pada indeks musim kemarau Keetch-Byram (KBDI-Keetch Byram Drought Indeks). KPH Semarang secara geografis terletak pada 3'35' - 3'59' BT dan 7'00' - 7'15' LS merupakan wilayah yang dinilai cukup rawan terhadap kebakaran hutan. Penelitian ini dilakukan untuk menentukan musim kebakaran hutan di KPH Semarang, nilai bahaya kebakaran hUIan di KPH Semarang dengan Keetch-Byram Drought Index, hubungan antara KBDl dengan luas dan frekuensi kebakaran hUIan di Semarang dan hubungan antara unsur-unsur cuaca dengan luas dan frekuensi kebakaran hurnn. Manfaat dari penelitian ini adalah memberikan infonnasi harian mengenai tingkat kerawanan kebakaran hUIan di Semarang, Jaws Tengah untuk mendukung upaya kegiatan pencegahan terutama deteksi kebakaran hutan serta tindakan yang perlu diarnbil dalam pengendalian kebakaran hutan.
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Judul: Penilaian bahaya kebakaran hutan dengan menggunakan Indeks Kekeringan Keetch Byram (Keetch Byram Drought Index / KBDI) di RPH Sumberkima Propinsi Bali
Abstrak: Dalam usaha pencegahan dan penanggulangan kebakaran hutan diperlukan suatu manajemen pengendalian kebakaran hutan (forest fire management). Salah satu upaya untuk melakukan pencegahan kebakaran hutan ini yaitu dengan melakukan Penilaian Bahaya Kebakaran Hutan (Fire Danger Rating). Metode Penilaian Bahaya Kebakaran telah banyak dikembangkan untuk menilai tingkat bahaya kebakaran. Indeks Kekeringan Keetch-Byram (KBDI) baru-baru ini dikembangkan untuk menilai bahaya kebakaran hutan di Kalimantan Timur. Menurut Deeming (1995), sistem ini dinilai dapat diandalkan sebagai model yang diusulkan dalam sistem penilaian bahaya kebakaran. Keuntungan KBDI terletak pada pengumpulan data yang semuanya bisa diperoleh di stasiun klimatologi, yang berupa rata-rata curah hujan tahunan, curah hujan harian dan temperatur harian maksimum. Selain itu KBDI dapat dihitung secara manual dan persamaannya diprogram ke dalam kalkulator atau komputer. Penelitian bertujuan untuk menilai bahaya kebakaran hutan pada hutan tanaman dan lapangan terbuka serta penerapan KBDI sebagai Early Warning System di Propinsi Bali.
Keyword: forest fire

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