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: Modification and  Performance Test of The Wick Stove Fueled by Waste Cooking Oil
Abstrak: Penggunaan minyak jelantah sebagai bahan bakar kompor sumbu merupakan salah satu solusi pemanfaatan limbah minyak jelantah. Minyak jelantah memiliki viskositas tinggi sehingga minyak sulit naik pada sumbu kompor. Hasil uji pendahuluan menunjukkan kinerja awal kompor rendah dengan efisiensi 16%. Penelitian ini bertujuan meningkatkan efisiensi energi dari kompor sumbu minyak jelantah dengan cara memodifikasi bahan sumbu dan sistem inlet udara serta melakukan uji kinerja kompor yang telah dimodifikasi tersebut. Metode yang digunakan adalah uji kapilaritas sumbu, penyesuaian luas inlet udara berdasarkan perhitungan kebutuhan udara pembakaran, dan water boiling test. Bahan sumbu yang sesuai karakteristik minyak jelantah adalah benang katun dengan nilai kapilaritas 1.06 g/menit. Diameter lubang inlet udara diperbesar dari 1 mm menjadi 3 mm dan 5 mm. Kinerja kompor terbaik ditunjukkan oleh kompor dengan modifikasi sumbu saja, dimana efisiensi sistem meningkat dari 16% menjadi 26%, laju pembakaran meningkat dari 0.35 kg/jam menjadi 0.42 kg/jam, dan efisiensi kompor meningkat dari 24% menjadi 40%. Kinerja kompor dengan penerapan modifikasi sumbu dan inlet udara berhasil meningkatkan efisiensi sistem menjadi 22% dibandingkan sebelum modifikasi, dengan laju pembakaran 0.40 kg/jam, dan efisiensi kompor 38%. Tetapi kinerja ini lebih rendah dibanding penerapan modifikasi sumbu saja, karena penambahan luas inlet udara tidak diiringi peningkatan kontak udara dengan bahan bakar.
Keyword: modify, waste cooking oil, wick stove

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