Judul: Karakterisasi Bioarang Batang Kelapa Sawit dengan Variasi Bagian Batang dan Waktu Tinggal
Abstrak: Batang kelapa sawit adalah salah satu sumber energi terbarukan yang potensial. Batang kelapa sawit mempunyai beberapa kekurangan, yaitu kadar air dan kadar zat volatil yang tinggi, serta nilai kalor yang rendah. Untuk mengatasi permasalahan tersebut, batang kelapa sawit perlu diolah terlebih dahulu melalui proses torefaksi, yaitu sebuah metode termokimia untuk mengubahnya menjadi bioarang pada suhu 200-300°C pada lingkungan inert. Tujuan dari penelitian ini adalah mempelajari karakteristik bioarang yang dibuat dari berbagai macam bagian batang (aksial dan radial) dan waktu tinggal (60, 90, dan 120 menit). Hasil penelitian bahwa, terdapat variabilitas dari kadar air, zat volatil, abu, dan karbon terikat pada bioarang yang dibuat dari sepanjang bagian batang. Peningkatan waktu tinggal menyebabkan penurunan kadar air dan kadar zat volatil, dan peningkatan kadar abu dan kadar karbon terikat. Karbon terikat memiliki pengaruh yang signifikan terhadap nilai kalor. Kadar air, kadar zat volatil, kadar abu dan kadar karbon terikat bioarang masing-masing adalah 3.52-6.00%, 35.50-41.75%, 3.57- 13.29%, dan 46.53-51.44%,
Keyword: abu, kadar air, karbon terikat, nilai kalor, torefaksi, dan zat volatil

Judul: Biopelet Arang Torefaksi Batang Kelapa Sawit.
Abstrak: Limbah batang kelapa sawit berpotensi tinggi sebagai bahan baku energi. Penelitian ini bertujuan untuk menentukan mutu biopelet yang dibuat dari bagian bawah dan tengah batang kelapa sawit dengan menggunakan lindi pengarangan sebagai perekat. Sebelum biopeletisasi, bahan baku ditorefaksi suhu 200 oC. Mutu biopelet ditentukan berdasarkan kadar air, zat terbang, kadar abu, karbon terikat, ketahanan dan nilai kalor biopelet yang ditentukan mengacu pada standar DIN EN 14961-2 dan SNI 01-6235-2000. Hasil penelitian menunjukkan bahwa biopelet memiliki kadar air, kadar zat terbang, kadar abu, karbon terikat, ketahanan dan nilai kalor berturut-turut 1.95-17.58%, 59.16-68.60%, 3.33-5.72%, 17.54-27.39%, 73.89-99.15%, dan 5304-5864 kkal/kg. Perlakuan torefaksi pada bahan baku dapat meningkatkan nilai kalor biopelet secara signifikan.
Keyword: batang kelapa sawit, biopelet, torefaksi

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