Judul: Study on carbon stock potential of tea plantation (Camelia sinensis (L) O. Kuntze) and land use cover in Gunung Halimun National Park, Nanggung, Bogor
Abstrak: Penelitian dilaksanakan di perkebunan PT Nirmala Agung dan sekitar kawasan hutan alam Taman Nasional Gunung Halimun di Kecamatan nanggung, Bogor. Pengumpulan data primer dilakukan dengan metode destruksi terhadap tanamn the, tumbuhan bawah dan serasah. jumlah tanaman teh yang didestruksi/dibongkar sebanyak 40 tanaman pada kelas umur 5, 10, 15 dan 20 tahun masing-masing 10 tanaman contoh. Penghitungan biomas pohon dengan persamaan dometrik Brown (1997) dan untuk pinus menggunakan metode JICA (2004)
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Judul: Estimation of Vegetation Density and Carbon Stock in the  Agroforestry Land of Gunung Halimun Salak National Park
Abstrak: Indonesia’s FOLU Net Sink merupakan salah satu komitmen pemerintah Indonesia dalam mengurangi emisi gas rumah kaca dan perubahan iklim melalui sektor kehutanan dan lahan. Penelitian ini bertujuan untuk i) menduga kerapatan vegetasi menggunakan Normalized Difference Vegetation Index (NDVI), ii) menduga biomassa dan cadangan karbon, serta iii) menganalisis hubungan NDVI dengan keanekaragaman vegetasi dan cadangan karbon di lahan agroforestri Resort Gunung Kendeng, TNGHS. Metode yang digunakan adalah analisis vegetasi dan estimasi karbon menggunakan pendekatan alometrik dan destruktif. Hasil analisis NDVI, kelas kerapatan vegetasi terbagi menjadi kelas 1 dan kelas 2. Komposisi vegetasi di lokasi penelitian terdiri atas 57 jenis tumbuhan dari 31 famili. Lokasi penelitian didominasi oleh Agathis dammara. Struktur horizontal vegetasi membentuk kurva “J” terbalik. Rata-rata biomassa dan cadangan karbon yang diperoleh masing-masing sebesar 52,80 ton/ha dan 24,81 ton C/ha. Konsentrasi karbon memiliki korelasi paling tinggi dengan LBDS (r = 92,1%). Nilai NDVI memiliki korelasi tertinggi dengan Indeks Shannon-Wiener.
Keyword: Agathis dammara, biomassa, FOLU Net Sink, indeks vegetasi

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