Judul: Identification of Forest Fire Severity and Estimation of Carbon Emissions in The Mount Ungaran Forest Area, Central Java
Abstrak: Kebakaran hutan yang terjadi di dataran tinggi di wilayah tropis mengakibatkan peningkatan ancaman terhadap keberadaan hutan. Kawasan Hutan Gunung Ungaran (KHGU) merupakan hutan pegunungan yang pernah terjadi kebakaran hutan pada tahun 2018. Penelitian ini bertujuan menganalisis tingkat keparahan kebakaran hutan, menganalisis komposisi vegetasi, dan mengukur biomassa, karbon, serta emisi karbon yang dihasilkan pascakebakaran di KHGU. Metode pada penelitian ini menggunakan data primer dan data sekunder. Hasil penelitian menunjukkan bahwa tingkat keparahan kebakaran hutan di Puncak Botak terbagi menjadi tiga kelas yaitu berat, sedang, dan tidak terbakar. Luas area tingkat keparahan kebakaran untuk kelas berat, sedang, dan tidak terbakar masing-masing seluas 15,1 ha, 22,1 ha, dan 3.830,2 ha. Jenis yang dominan di Puncak Botak yaitu alang-alang (Imperata cylindrica) sedangkan di Puncak Ungaran pada tingkat semai yaitu blabahan (Brachiaria paspaloides), tingkat pancang yaitu kayu tejo (Manglietia glauca), tingkat tiang dan pohon yaitu kayu sarangan (Castanopsis argentea). Jumlah biomassa dan karbon di Puncak Botak diperoleh masing-masing sebesar 53,95 ton/ha; 25,34 ton/ha sedangkan di Puncak Ungaran diperoleh sebesar 5,55 ton/ha; 2,56 ton/ha. Emisi karbon yang dihasilkan akibat kebakaran di Puncak Botak yaitu sebesar 0,022 tonCO₂. Kata kunci: Gunung Ungaran, Puncak Botak, Puncak Ungaran, indeks NBR, Sentinel-2A, Forest fires occurring at high altitudes in the tropics pose an increasing threat to the existence of forests. The Mount Ungaran Forest Area (KHGU) is a mountain forest that experienced forest fires in 2018. This study aimed to analyse the severity of forest fires, assess vegetation composition, and measure post-fire biomass, carbon and carbon emissions in KHGU. This research method used primary data and secondary data. The results showed that the severity of forest fires in Botak Peak was divided into three classes: heavy, medium, and unburned. The area of fire severity for heavy, moderate and unburnt classes are 15,1 ha, 22,1 ha and 3.830,2 ha respectively. The dominant species at Botak Peak is alang-alang (Imperata cylindrica) while at Ungaran Peak at the seedling level is blabahan (Brachiaria paspaloides), stake level is tejo wood (Manglietia glauca), pole and tree level is sarangan wood (Castanopsis argentea). The amount of biomass and carbon at Botak Peak was 53,95 tonnes/ha; 25,34 tonnes/ha respectively while at Ungaran Peak it was 5,55 tonnes/ha; 2,56 tonnes/ha. The carbon emissions generated by the fire on Botak Peak are 0,022 tonnesCO₂. Keywords: Mount Ungaran, Botak Peak, Ungaran Peak, NBR index, Sentinel-2A
Keyword: Mount Ungaran, Botak Peak, Ungaran Peak, NBR index, Sentinel-2A

Judul: Estimation of carbondioxide Emission Of Forest and Land Fire in Tanjung Jabung Timur Regency,  Jambi Province
Abstrak: Kebakaran lahan dan hutan berkontribusi besar pada peningkatan emisi karbon di atmosfer bumi. Emisi karbon ini dapat berdampak buruk terhadap lingkungan. Penelitian ini difokuskan dalam estimasi luas area kebakaran dan estimasi emisi karbon di Kabupaten Tanjung Jabung Timur, Provinsi Jambi. Penelitian dilakukan dengan metode digital untuk menduga luas area terbakar. Peta luasan kebakaran diperoleh dengan menggunakan citra Landsat 8 OLI/TIRS Tier 2. Penghitungan emisi karbondioksida menggunakan persamaan Seiler dan Crutzen. Fluktuasi jumlah hotspots yang terdeteksi pada tahun 2016-2020 menyebabkan perbedaan luas area yang terbakar, jumlah hotspots tertinggi diperoleh pada tahun 2019, sebanyak 6.839 titik pada tanah gambut dan 1.529 titik pada tanah mineral dan merupakan area terluas yang terbakar yaitu sebesar (7.758,17 ha) pada tanah gambut dan (9.434,56 ha) pada tanah mineral. Penelitian ini menunjukkan bahwa besarnya emisi dari suatu wilayah dipengaruhi oleh kandungan biomassa dan luasan area terbakar. Emisi gas CO2 terbesar diperoleh pada tahun 2019 sebesar 507.433,1 ton CO2 pada tanah gambut dan 103.048,2 ton CO2 pada tanah mineral ., Land and forest fires contribute greatly to the increase in carbon emissions in the earth's atmosphere. These carbon emissions may bring negative impacts on the environment. This research is focused on estimating the burnt area and estimating carbon emissions in Tanjung Jabung Timur Regency, Jambi Province. The research was conducted using a digital method to estimate the burned area. A burnt area map was made using Landsat 8 OLI/TIRS Tier 2 images. The carbon emission was calculated by using Seiler and Crutzen (1980) equation. Fluctuations in the number of hotspotss detected in 2016-2020 led to differences in the area burned during the period. The highest number of hotspots found in 2019, detected as many as 6.839 points on peat lands and 1.529 points on mineral soil and was the largest area burned was (7.758,17 ha) on peat lands and (9.343.56 ha) on mineral soil . This research proves that the amount of emissions from an area is greatly influenced by the biomass and burnt area. The largest CO2 gas emissions was found in 2019 which was 507.433,1 ton CO2 on peat lands and 103.048,2 ton CO2 on mineral soils.
Keyword: carbondioxide emission, hotspots, land and forest fire, Tanjung Jabung Timur

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