Judul: Study of Cultivation Techniques, Flowering  Phenology, Production and Quality of Essential Oil of Cananga Cananga odorata forma macrophylla and forma fruticosa
Abstrak: Bunga kenanga (Cananga odorata) merupakan salah satu sumber minyak atsiri yang banyak digunakan di industri parfum dan farmasi. Jawa pernah dikenal sebagai penghasil minyak kenanga di dunia dengan Desa Gajahrejo, Kecamatan Purwodadi, Kabupaten Pasuruan sebagai salah satu sentra budidaya kenanga. Praktik budidaya kenanga yang baik sangat penting dalam menjaga kontinuitas produksi dan kualitas bunga. Penelitian yang mengidentifikasi karakter dari berbagai varietas dan aksesi bunga kenanga di Indonesia belum banyak dilakukan. Penelitian ini bertujuan mendapatkan informasi budidaya, karakterisasi tanaman, fenologi pembungaan, dan rendemen minyak dari dua jenis kenanga di Gajahrejo. Kenanga yang dibudidayakan adalah kenanga pohon (Cananga odorata forma macrophylla) dan kenanga perdu (Cananga odorata forma fruticosa). Faktor yang memiliki pengaruh pada produksi bunga kenanga perdu adalah jumlah cabang produktif sehingga kegiatan pemangkasan pucuk pada kenanga memiliki pengaruh yang baik. Bunga kenanga pohon dapat digunakan untuk bunga kuburan dan bahan baku penyulingan minyak serta memiliki helai bunga lebih besar dibandingkan bunga kenanga perdu. Durasi perkembangan bunga kenanga pohon adalah 44 hari sedangkan kenanga perdu 42 hari. Rendemen minyak dari bunga kenanga pohon lebih besar yaitu 1,10% dibandingkan bunga kenanga perdu yaitu 0,61%. Jika dilihat dari warna dan aroma minyak, keduanya berwarna kuning pucat dan harum, tapi minyak bunga kenanga perdu memiliki aroma yang lebih wangi daripada minyak bunga kenanga pohon.
Keyword: budidaya, fenologi bunga, karakterisasi, minyak bunga, rendemen minyak

Judul: Pengaruh Perajangan Bunga Kenanga (Canangium odorotum Baill), Kepadatan dan Metode Penyulingan Terhadap Rendemen dan Mutu Minyak Kenanga yang Dihasilkan
Abstrak: Indonesia is one of the developing countries which have the potency to export cananga oil. About 50 tons cananga oil from Indonesia every year exported to Europe and American. Cananga oil Producer area in Indonesia for example regency Boyolali, Cirebon, Banten And Blitar. In this time in Indonesia the ordinary distillation method done some distillation generally by using water distillation and is previously done by slice. This research aim are to studying influence slice of the sliced cananga flower, distillation method and substance density. Others, to produce optimal yield and quality of cananga oil. The Experimental design were subjected to an analysis of variance for a completely random design 3x2x2 with duplo procedure. The treatment are the bulk density of cananga flower in the vessel (0.075 kg/L, 0.0875 kg/L dan 0.1 kg/L), the distillation method of cananga flower (water distillation, water and steam distillation), and sliced flower before distillation and without slice. This research done in 2 step, the first preliminary research consist of analysis water content (82%), cananga oil content (1.6 %) and look for the optimum condition from distillation of cananga oil. The second step is the main research were done distillation according to treatment which have been specified . Interaction of distillation method and slicing of cananga flower has influence in evaporation residu number and acid number. Interaction of distillation method, slice of cananga flower, and bulk density of flower has influenced to acid number. Factor of distillation didn’t influence to specific gravity and index of refraction. Factor of slicing of cananga flower didn’t influence to specific gravity, index of refraction, optical rotation, ester number and acid number. Factor of bulk density of flower didn’t influence to specific gravity, index of refraction and optical rotation. Interaction of distillation method and slicing of cananga flower didn’t influence to yield, specific gravity, index of refraction, optical rotation, and ester number. Interaction of distillation method and bulk density of flower and interaction of bulk density of flower and slicing of cananga didn’t influence to quality of the cananga oil. Interaction from three factor didn’t influence to specific gravity, index of refraction, optical rotation, ester number, evaporation residu number, and yield of cananga oil. Progressively the increasing of bulk density will tend to decrease yield, specific grafity, index refraction, optical rotation, and the ester number, but tend to increase value of evaporation residu and acid number. All cananga oil are yielded soluble in etanol 95% with ratio 1:1 to 1:10. Water and steam distillation yielded cananga oil with value more height than water distillation in yield, specific grafity, index refraction, optical rotation, and acid number. Flower with slicing yielded cananga oil with value more height than flower without slicing in specific grafity, index refraction, optical rotation, ester number and evaporation residu number. Based on the GCMS analysis showed that the component which implied in cananga oil consist of Linalool, Geranyl acetate, beta.-Caryophyllene, alpha.- Caryophyllene, Germacrene-D, alpha.-Bergamotene, delta.-Cadinene, Benzyl benzoate dan Cis-Farnesol amounted to 80.82 %, as for minor component amounted to 19.18% consist of monoterpen and benzena and part of Monoterpen O, seskuiterpen,
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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