Judul: Optimasi Emisi Nanopartikel Karbon (C-dots) dari Limbah Bungkil Kelapa Sawit dan Serat Kulit Durian secara Hidrotermal.
Abstrak: Nanopartikel karbon merupakan nanomaterial berfluoresens berukuran kurang dari 10 nm. Berdasarkan penelitian sebelumnya C-dots dari sumber karbon bungkil kelapa sawit dan serat kulit durian secara hidrotermal pada suhu 180 ℃ selama 12 jam menghasilkan intensitas emisi yang sangat rendah, yaitu 2% dengan kadar urea 0% dan intensitas emisi meningkat menjadi 6% dengan kadar urea 50%. Oleh karena itu tujuan dari penelitian ini ialah menentukan kondisi optimum sintesis C- dots dari kedua sumber karbon tersebut secara hidrotermal. Rendemen tertinggi pada C-dots bungkil 20% pada kondisi: suhu 160 ℃12 jam urea 25%, sedangkan rendemen tertinggi pada C-dots serat ialah 14% pada kondisi: suhu 200 ℃ 6 jamurea 50%. Kondisi optimum berdasarkan intensitas pendaran yang dihasilkan C-dots bungkil ialah suhu 160 ℃ 6 jamurea 50%, sedangkan untuk C-dots serat kulit durian: suhu 180 ℃6 jamurea 25 %. Berdasarkan kajian pustaka, intensitas pendaran C-dots serat lebih tinggi dibandingkan C-dot bungkil disebabkan oleh keberadaan gugus fungsi tambahan, yaitu S=O.
Keyword: bungkil kelapa sawit, C-dots, fluoresens, hidrotermal, serat durian

Judul: Sintesis Nanopartikel Karbon (C-Dot) dari Buah Durian secara Hidrotermal dan Mikrogelombang
Abstrak: Kulit durian adalah biomassa dari limbah pertanian dengan komposisi selulosa, lignin, dan protein, dapat dijadikan sebagai sumber karbon dot (C-Dot). C–dot merupakan material yang termasuk ke dalam kelas nanopartikel dimensi 0 (zero dimensional). Penelitian ini bertujuan menyintesis C-dot dari duri, serat, dan daging buah durian menggunakan metode hidrotermal dan mikrogelombang. Nanopartikel C-Dot disintesis dengan tambahan dan tanpa tambahan urea. Rendemen C-Dot dari duri, serat, dan daging durian menggunakan metode hidrotermal dengan tambahan urea berturut-turut adalah 3.71%, 3.40%, 6.21%, dan tanpa tambahan urea berturut-turut adalah 2.98%, 2.90%, 4.78%. Rendemen C-Dot dari metode mikrogelombang dengan tambahan urea berturut-turut 14.30%, 13.40%, 16.63%, dan tanpa tambahan urea adalah 1.41%; 1.38%; 3,17%. C-Dot yang dihasilkan berwarna hitam. Tambahan urea dapat meningkatkan kelarutan, dan intensitas pendaran C-Dot. Spektrum inframerah menunjukkan keberadaan gugus karboksil dan amina untuk sampel C-Dot dari serat dengan tambahan urea. Karakterisasi dengan SEM EDX menunjukkan komposisi C-Dot, dengan 34,77% C dan 43,95% N. C-Dot terbaik berdasarkan intensitas pendaran diperoleh dari serat kulit buah durian.
Keyword: C-Dots, emissions, fluorescence, SEM EDX

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