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: Fire Control System in Gunung Walat Education Forest, Sukabumi, West Jawa
Abstrak: Kebakaran hutan terjadi setiap tahun di Indonesia dengan frekuensi, intensitas, dan luas areal terbakar yang berbeda. Hutan Pendidikan Gunung Walat (HPGW) termasuk ke dalam Kawasan Hutan dengan Tujuan Khusus (KHDTK) yang dikelola oleh Fakultas Kehutanan dan Lingkungan IPB. HPGW merupakan salah satu kawasan hutan di Jawa Barat yang hampir setiap tahunnya mengalami kebakaran. Penelitian ini bertujuan mengidentifikasi, mendeskripsikan, dan menganalisis faktor penyebab terjadinya kebakaran hutan, tingkat kerawanan kebakaran hutan di HPGW berdasarkan curah hujan, serta sistem pengendalian kebakaran hutan yang diterapkan di HPGW. Metode analisis data yang digunakan adalah deskriptif kualitatif dengan mendeskripsikan semua fakta yang diperoleh. Pembukaan lahan dengan membakar oleh masyarakat menjadi penyebab utama kebakaran hutan di HPGW. Hasil analisis curah hujan untuk menentukan tingkat kerawanan kawasan terhadap kebakaran menunjukkan tingkat kerawanan yang rendah dengan nilai 2805,5 mm/tahun. Bahan bakar potensial di kawasan ini antara lain, tegakan jenis pinus dan agatis serta serasah daun pinus. Sistem pengendalian kebakaran hutan di HPGW terdiri atas empat komponen yaitu pencegahan, pra pemadaman, pemadaman, dan penanganan pasca kebakaran hutan., Forest fires occur every year in Indonesia with different frequency, intensity, and area burned. Gunung Walat Educational Forest (HPGW) is included in the Special Purpose Forest Area (KHDTK) managed by the Faculty of Forestry and Environment, IPB. It is one of the forest areas in West Java that experiences forest fires almost every year. The purposes of this research are to identify, describe, and analyze the factors that cause forest fires, the potential for fuel, the level of forest fire vulnerability in the HPGW based on rainfall, and the forest fire control system implemented in the HPGW. Data analysis method is descriptive qualitative by describing all the facts obtained. Land clearing by burning by people is the main cause of forest fires in HPGW. The results of the rainfall analysis to determine the area's vulnerability to fire showed a low level of vulnerability with a value of 2805.5 mm/year. There ara two species that can be potential fuels in this area, namely pine and agatis species, and also pine leaf litter. The forest fire control system in HPGW consists of four components, namely prevention, pre-extinguishing, extinguishing, and post-forest fire management.
Keyword: Fire causes, fire control, Gunung Walat, Educational Forest, rainfall

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