Judul: Pencirian Minyak Sereh Wangi Mahapengiri (Cymbopogon winteranus Jowitt) Klon G1, G2, dan G3 Menggunakan Kromatograf Gas-Spektrometer Massa
Abstrak: Citronellal (Cymbopogon sp) oil is one of the Indonesia's essential oils export commodities. To maintain export quality, in 1989 three types of mahapengiri citronellal (C. winteranus Jowitt) G1, G2, and G3 superior clones were developed. In this experiment, the characteristics of major and minor chemical components within each clone were determined in order to develop formulations of oil fuel bioadditive. The study was started with steam distillation of citronellal leaves of clone G1, G2, and G3. The oil obtained were tested for the physicochemical properties, and the chemical compositions were analyzed with gas chromatograph-mass spectrometer. The results showed that the physicochemical properties of citronellal oil were in accordance with SNI 06-3987-1995. The major chemical constituents of citronellal oil were citronellal, citronellol, geraniol, limonene, β-caryophylene, and citronelyl acetate. The minor chemical constituents included D-germacrene, α-amorphene, endo-1-bourbonanol, geranyl acetate, linalool, δ-cadinene, elemol, β-elemene, (Z, E)-α-farnesene, α-cardinole, and α-humulene.
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Judul: Yield and Quality of Lemongrass  (Cymbopogon nardus L.) Essential Oil at Various Harvest Ages and Chopping  Materials
Abstrak: Indonesia merupakan salah satu negara penghasil minyak atsiri terbesar di dunia. Minyak atsiri merupakan salah satu jenis minyak nabati yang multimanfaat dan berpotensi ekspor. Minyak ini diperoleh dari berbagai tanaman seperti nilam, akar wangi, pala, cengkeh, dan serai wangi. Salah satu jenis komoditas minyak atsiri ekspor unggulan Indonesia adalah minyak serai wangi. Penelitian ini bertujuan mengkaji pengaruh umur panen dan perajangan bahan terhadap rendemen dan mutu minyak serai wangi. Bahan yang digunakan adalah daun serai wangi yang diperoleh dari kebun serai wangi milik PT Harmonis Permaculture yang terletak di desa Karacak kecamatan Leuwiliang. Sebanyak 3 kg daun serai wangi untuk tiap perlakuan diangin-anginkan selama 24 jam sebelum dilakukan proses penyulingan. Penyulingan dilakukan selama 4 jam dihitung sejak distilat keluar dari kondensor. Minyak atsiri yang dihasilkan dianalisis meliputi rendemen, densitas (bobot jenis), indeks bias, dan warna. Penelitian ini menggunakan rancangan acak lengkap faktorial dengan dua faktor yaitu umur panen (1,5 bulan, 2 bulan, dan 3 bulan) dan perajangan bahan (perajangan dan tanpa perajangan). Hasil penelitian menunjukkan bahwa umur panen dan perajangan bahan berpengaruh nyata terhadap rendemen, namun tidak berpengaruh nyata terhadap mutu minyak serai wangi. Hasil uji posthoc Duncan menunjukkan adanya peningkatan rendemen minyak yang signifikan secara statistik seiring dengan dilakukannya proses perajangan bahan dan bertambahnya umur panen. Hasil terbaik diperoleh pada umur panen 3 bulan dengan perajangan menghasilkan rendemen 1,08%, densitas 0,907 g/cm3 , indeks bias 1,467, dan warna kuning cenderung jernih. Secara keseluruhan mutu minyak serai wangi yang dihasilkan sesuai dengan SNI No.06-3953-1995 untuk persyaratan ekspor.
Keyword: citronella oil, distillation, harvest time, quality, yield

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