Judul: Composition and Vegetation Structure after Fire in the Peat Hydrologycal Unit (PHU) Meranti Island, Riau
Abstrak: Kebakaran berdampak pada perubahan komposisi jenis penyusun hutan, terutama pada hutan gambut yang memiliki ekosistem unik. Penelitian ini bertujuan untuk mengidentifikasi komposisi jenis di lahan pasca kebakaran pada perbedaan tahun kebakaran serta frekuensi kebakaran. Identifikasi area bekas kebakaran memanfaatkan indeks Normalized Burn Ratio (NBR) dan nilai difference Normalized Burn Ratio (dNBR). Data hotspot MODIS digunakan sebagai data pendukung area kebakaran serta dimanfaatkan sebagai penentuan periode kebakaran. Penelitian dilakukan di Kepulauan Meranti, Riau dengan pengumpulan data di lapangan menggunakan teknik purposive sampling pada kondisi hutan pasca kebakaran tahun 2014, 2018 dan 2019, serta 2020. Keanekaragaman jenis pada lahan pasca kebakaran tahun 2014 ditemukan 10 spesies, pada lahan pasca kebakaran keterulangan 2018 dan 2019 diperoleh 9 spesies, sedangkan pada lahan pasca kebakaran tahun 2020 ditemukan 7 spesies. Hasil suksesi pasca kebakaran hanya mencapai tingkat permudaan pancang. Tingkat tiang dan pohon yang ditemukan merupakan individu yang tahan terhadap kebakaran dan bukan merupakan hasil suksesi. Disisi lain, Asplenium sp. merupakan spesies tingkat tumbuhan bawah yang mendominasi di seluruh petak contoh.
Keyword: hotspot, kebakaran, lahan gambut, NBR, suksesi

Judul: Analysis of Vegetation and Drought Conditions in Peatland following 2015’s Fires in Kepulauan Meranti
Abstrak: Lahan gambut di Indonesia banyak yang mengalami degradasi menjadi lahan tidak produktif. Kerusakan lahan gambut berdampak pada penurunan kualitas ekosistem gambut, penurunan keanekaragaman hayati, peningkatan risiko kebakaran, serta peningkatan emisi gas rumah kaca ke atmosfer. Tujuan dari penelitian ini untuk menganalisis kondisi vegetasi dan kekeringan pada lahan gambut pasca kebakaran hutan dan lahan tahun 2015. Penelitian mengambil kasus kebakaran di Pulau Padang, Kepulauan Meranti, Riau. Penelitian menggunakan data citra satelit Landsat-8 surface reflectance (SR) untuk menentukan indeks vegetasi dan kekeringan yaitu: Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), dan Normalized Multi-band Drought Index (NMDI). Hasil penelitian menunjukkan bahwa NBR merekam wilayah dengan tingkat kebakaran yang tinggi di tahun 2014, 2015, dan 2020. Hal tersebut didukung dengan sebaran vegetasi secara spasial yang ditunjukkan oleh citra NDVI. Nilai rata-rata NDVI yang rendah menunjukkan sebagian besar wilayah kajian merupakan lahan minim vegetasi . Sebagian besar area citra NMDI di lahan kosong menunjukkan kondisi kering dan sangat kering dengan nilai NMDI setiap tahun (2014-2020) yang tinggi. Tahun pre-fire 2019 memiliki cakupan area tertinggi untuk kondisi lahan sangat kering. Meski demikian, laju pertumbuhan vegetasi menunjukkan tren peningkatan setelah periode kebakaran., Most peatlands in Indonesia are degrading into unproductive land. Damage to peatlands decreases the quality of the peat ecosystem, decreasing biodiversity, increasing the risk of fire, and increasing greenhouse gas emissions into the atmosphere. This study aims to analyze the condition of vegetation and drought on peatlands after the 2015 fire in Padang Island, Meranti Islands, Riau. The method was processed by performing Landsat 8 surface reflectance (SR) image data to determine: Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), and Normalized Multi-band Drought Index (NMDI). The results of this study indicate that NBR recorded areas with high fire rates in 2014, 2015, and 2020. Validate by the spatial distribution of vegetation shown by NDVI images. The low average NDVI value indicates that most of the study area is land with minimal vegetation. Most of the NMDI image areas in vacant land show a dry and extreme dry condition with high NMDI values every year (2014-2020). The pre-fire year 2019 had the highest area coverage for extreme dry land conditions. However, the vegetation growth rate showed an increase after the fire period.
Keyword: burn severity, hotspot, plants water content, soil moisture, spatial index

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