Judul: Modifikasi dan Unjuk Kerja Kompor Sumbu Tunggal Berbahan Bakar Minyak Bintaro
Abstrak: Bintaro (Cerbera odollam gaertn) has oil content 46-64% and it is potential as a biofuel feedstock. Bintaro oil, like other bio oil, has high viscosity. Due to it, the capillarity of oil on the wick is very low. This will affect to the combustion. The objectives of this study were to analyze the properties of Bintaro oil and to examine the use of Bintaro oil as fuel for a single wick stove. In this study, sample of Bintaro oil was processed from Bintaro fruits collected around Bogor. Sample of single wick stove which was familiar stove used by household in Bogor area, was modified mainly at part of flame holder at the height of 5, 6, 7, 8 and 11 cm. The caracteristic of combustion was investigated from the flames temperature and contour at different height of flame holder. The result showed that heat loss rate values for Bintaro oil were 76.6 W, 88.5 W, 100.7 W, 111.1 W and 142.8 W for the height flame holder of 5, 6, 7, 8 and 11 cm. The flame temperature based on the heat loses were 443.97, 476.75, 451.95, 450.5 and 454.77°C, respectively. For kerosene, the heat loss and flame temperature were 245.6 W and 730.77 °C for the height flame holder of 11 cm. The boiling test resulted that for kerosene with flame holder of 11 cm ,the water boiled at temperature of 100°C for 26 minutes. The average temperature was 637 °C and the power was 1.318 W. For Bintaro oil with the height of flame holder of 6 cm, resulted the flame temperature of 482°C, the heat loss of 1.092 W, and the water boiled of 45 minutes.
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Judul: Performance test on bio oil stove using calophyllum oil and cerbera oil as the fuel
Abstrak: Bio oil stove is a wick stove which was modified from kerosene stove, and designed for vegetable oil as its fuel. The objective of the research was to conduct performance test of the stove by using Cerbera oil and Callophylum oil as fuel. The result shows that the heat capacity of the stove by using Callophylum oil as fuel was 172.58 kJ/minute, and by using Cerbera oil was 199.95 kJ/minute. The total fuel consumption were 0.12 kg for Callophylum oil usages and 0.17 kg for Cerbera oil usages. Stove efficiency and system efficiency with Callophylum oil usages were 33.64% and 13.96%. While, Callophylum oil usages only 19.39% and 9.28%., Kompor minyak nabati merupakan jenis kompor bersumbu banyak (multi-wick) yang menggunakan bahan bakar minyak nabati sebagai bahan bakarnya, kompor ini merupakan modifikasi dari kompor minyak tanah. Penelitian ini bertujuan untuk menguji kinerja kompor minyak nabati dengan menggunakan minyak bintaro dan minyak nyamplung. Hasil pengujian menunjukkan kapasitas panas yang dihasilkan oleh kompor minyak nabati dengan bahan bakar minyak nyamplung adalah 172.58 kJ/menit dan dengan bahan bakar minyak bintaro 199.95 kJ/menit. Konsumsi bahan bakar total kompor minyak nabati dengan bahan bakar minyak nyamplung dan minyak bintaro adalah masing-masing 0.12 kg dan 0.17 kg. Efisiensi kompor dan efisiensi sistem kompor minyak nabati dengan bahan bakar minyak nyamplung adalah 33.64% dan 13.96%. Sedangkan efisiensi kompor dan efisiensi sistem dengan bahan bakar minyak bintaro adalah 19.39% dan 9.28%.
Keyword: vegetable oil, stove, efficiency, Cerbera, Callophylum

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