Judul: Dampak Perubahan Luas Mangrove di Papua dan Papua Barat terhadap Emisi Karbon.
Abstrak: Mangrove adalah ekosistem hutan pantai yang unik karena kemampuannya dalam menyerap dan menyimpan karbon atmosfer dan perairan dalam jumlah besar bahkan jika dibandingkan dengan tipe hutan lain. Namun keberadaannya di Papua dan Papua Barat kini terus mengalami penurunan luas akibat faktor antropogenik (perubahan fungsi lahan) dan faktor alam. Oleh sebab itu perlu dilakukan pengawasan dan pelaporan kondisi mangrove hubungannya dengan dampak yang ditimbulkan akibat perubahan luas mangrove terhadap pelepasan sejumlah emisi karbon ke atmosfer dalam jangka waktu yang lama. Penelitian yang dilakukan memiliki tujuan (i) mengidentifikasi penyebaran luas mangrove, (ii) menduga laju penurunan luas dan konsekuensinya terhadap cadangan karbon menurut tipe hidrogeomorfik, dan (iii) menduga emisi karbon di atas permukaan tanah setiap kabupaten akibat perubahan tutupan lahan mangrove dekade pertama (2001-2010) dan kedua (2011-2018) dengan memanfaatkan Atlas Papua dan pendekatan beda-cadangan (stock-difference approach) di setiap tipe hidrogeomorfik. Berdasarkan hasil analisis diketahui bahwa Kabupaten Teluk Bintuni memiliki persentase luas mangrove terbesar yaitu 19% (228.419 ha) dan Kabupaten Tambrauw memiliki luas terkecil 0,002% (10 ha) dari total seluruh luas mangrove di Papua dan Papua Barat sebesar 1.174.947 ha pada tahun 2018. Luasan mangrove terdeteksi mengalami penurunan luas akibat perubahan fungsi lahan menjadi badan air, kawasan penambangan, daerah penebangan kayu, dan tutupan lahan lain seperti pemukiman hingga pelebaran jalan dengan laju 0,16%/th. Hal tersebut mengakibatkan terjadinya pengurangan cadangan karbon di atas permukaan dengan laju 10,52±8,60 Gg C/th atau 19,30±15,78 Gg CO2 ha/th. Emisi CO2 rata-rata dari deforestasi mangrove di Provinsi Papua dan Papua Barat tersebut hanya menyumbang 0.002% dari emisi nasional sektor FOLU sebesar 979.422 Gg CO2 ha/th.
Keyword: Atlas Papua, emisi karbon, hidrogeomorfik, kerapatan karbon, stock-difference

Judul: Cadangan Karbon Biomassa di Atas dan di Bawah Permukaan Tanah Mangrove serta Persebaran Spesies Global: Tinjauan Pustaka Sistematis
Abstrak: Mangrove merupakan ekosistem yang mampu menjadi pelindung wilayah pesisir untuk menahan abrasi serta secara ekologis dapat menyerap serta menyimpan karbon dalam ekosistem ini, termasuk pada biomassa di atas dan di bawah permukaan tanah. Fungsi penyerapan dan penyimpanan karbon oleh mangrove berpotensi sebagai solusi untuk mitigasi perubahan iklim berbasis alam. Namun, hutan mangrove mengalami degradasi yang diakibatkan oleh beberapa aktivitas seperti urbanisasi, pembangunan pesisir, akuakultur (tambak) dan proyek reklamasi. Tujuan dari penelitian ini adalah melakukan tinjauan dan sintesis skala global mengenai cadangan karbon biomassa di atas dan di bawah permukaan tanah, serta menginventarisasi persebaran spesies mangrove. Metode yang digunakan dalam penelitian ini adalah metode tinjauan pustaka sistematis, yaitu meninjau pustaka secara sistematis melalui penyaringan judul, abstrak, dan teks lengkap. Sebanyak 191 pustaka terjaring dari 6.864 pustaka yang sebelumnya diunduh dari laman Scopus dan Web of Science. Rata-rata cadangan karbon biomassa di atas pemukaan tanah secara global adalah 63,48 Mg C/ha, dengan wilayah Fiji sebagai wilayah yang memiliki nilai tertinggi (131,93 Mg C/ha). Rata-rata cadangan karbon biomassa di bawah permukaan tanah secara global senilai 31,71 Mg C/ha, dimana Colombia memiliki nilai tertinggi (117,30 Mg C/ha). Spesies mangrove secara global didominasi oleh Rhizophora spp., dengan keragaman spesies yang lebih bervariasi berada di wilayah Asia., Mangroves are ecosystem that can protect coastal areas from erosion and can ecologically absorb and store carbon in the ecosystem, including in the above-and below-ground biomass. The role of mangroves in carbon absorption and storage presents a potential natural-based solution to climate change mitigation. However, mangrove forests are facing degradation due to several activities, including urbanization, coastal development, aquaculture and reclamation projects. This research aims to review global carbon stocks especially in above-and below-ground biomass and inventory the distribution of mangrove species. The method employed in this study is a systematic literature review, involving the systematic screening of titles, abstracts, and full texts. A total of 191 pieces of literature were collected from the 6,863 pieces of literature retrieved from the Scopus and Web of Science databases. The average value of above-ground biomass carbon stocks globally is 63.48 Mg C/ha, with Fiji having the highest value (131.93 Mg C/ha). The average value of below-ground biomass carbon stocks globally is 31.71 Mg C/ha, with Colombia, recording the highest value (117.30 Mg C/ha). Globally, mangrove species are predominantly dominated by Rhizophora spp., with a more diverse species distribution observed in the Asian region.
Keyword: blue carbon, climate change, coastal, mitigation, wetlands

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