Judul: Estimated Value of Above Ground Carbon Stock in Community Forest (Study Case in Hutan Organik, Megamendung, Bogor District)
Abstrak: Pemanasan global disebabkan oleh banyaknya emisi gas rumah kaca yang dilepaskkan ke atmosfer, termasuk karbon dioksida (CO2). Penelitian ini bertujuan menduga cadangan karbon dan potensi serapan CO2 pada Kawasan Hutan Rakyat “Organik” Megamendung. Penelitian ini menggunakan metode stratified systematic sampling with random start dengan intensitas sampling 10% dan 35 plot contoh pengukuran tinggi serta diameter berukuran 0,04 ha. Cadangan karbon dan dan serapan CO2 ditentukan berdasarkan konversi 47% biomassa yang diperkirakan menggunakan persamaan alometrik. Hasil penelitian ini menunjukkan Kawasan Hutan Rakyat “Organik” Megamendung seluas 13,76 ha mampu menyimpan rata- rata cadangan karbon sebesar 24,14-43,18 ton/ha atau total cadangan karbon sebesar 469,25-481,88 ton (pada selang kepercayaan [SK] 95%). Kawasan Hutan Rakyat “Organik” Megamendung pada tahun 2021 ini telah menyerap total CO2 dari atmosfer sebesar 1722,16-1768,51ton (pada SK 95%) dengan kesalahan pendugaan sebesar 24,59%.
Keyword: allometric, biomass, CO2 absorption, globar warming, hutan rakyat “Organik”

Judul: Analysis of Carbon Absorption Potential Development in The Community Forest of Karacak Village, Bogor Regency, West Java
Abstrak: Hutan rakyat merupakan salah satu alternatif yang perlu diketahui kemampuannya dalam menyerap dan menyimpan karbon karena dalam perencaannya melibatkan masyarakat yang menguasai pengetahuan alam di daerahnya. Penelitian ini bertujuan untuk mengetahui pola pengelolaan dan dugaan cadangan serta kemampuan serapan karbon yang dimiliki hutan rakyat di Desa Karacak. Pengambilan data dilakukan kepada 30 plot contoh yang dibagi berdasarkan kelas kemiringan lahan. Hasil inventarisasi menunjukkan hutan Desa Karacak termasuk dalam tipe hutan rakyat kebun campuran dengan 8 jenis pohon penghasil non-kayu dan 2 pohon jenis penghasil kayu. Hasil pengolahan data menunjukkan dugaan rata-rata kemampuan cadangan karbon terbagi dalam kemiringan landai 36,23 tonC/ha, kemiringan bergelombang 37,98 tonC/ha dan kemiringan berbukit 38,65 tonC/ha dengan total rata-rata 37,62 tonC/ha. Dugaan rata-rata kemampuan serap karbon dioksida terbagi dalam kemiringan landai 7,62 tonCO2/ha/tahun, kemiringan bergelombang 7,73 tonCO2/ha/tahun dan kemiringan berbukit 6,69 tonCO2/ha/tahun dengan total rata-rata 7,35 tonCO2/ha/tahun., Community forests are one of the alternatives that need to be recognized for their carbon sequestration and reserves capability, as their planning involves the local community which possesses indigenous knowledge of their natural environment. This research aims to determine the management pattern and estimate carbon sequestration, as well as reserves capabilities of community forest in Karacak Village. The data collection was implemented on 30 sample plots, categorized based on the slope class. The inventory results indicate that the forest in Karacak Village falls under the category of mixed garden community forests, comprising 8 types of non-timber-producing trees and 2 types of timber-producing trees. Data processing results reveal an estimated average carbon reserve capacity distributed as follows: for flat terrain, 36,23 tons C/ha; undulating terrain, 37,98 tons C/ha; and hilly terrain, 38,65 tons C/ha, with a total average of 37,62 tons C/ha. The estimated average carbon dioxide sequestration capacity is stratified as follows: for flat terrain, 7,62 tons CO2/ha/year; undulating terrain, 7,73 tons CO2/ha/year; and hilly terrain, 6,69 tons CO2/ha/year, with a total average of 7,35 tons CO2/ha/year.
Keyword: carbon dioxide absorption, carbon stock, community forest

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