Judul: Characterization and technology process development of  standardized extract of kamandrah (Croton tiglium L.)
Abstrak: dentification and taxonomy analysis conducted at Herbariu m Bogoriense at Research Centre for Biology, Indonesian Institute of Sciences Bogor. The name of the plant was C.tiglium L. The moisture and proximate analysis showed that the kamandrah seed contained moisture up to 6.20%, fat 40.1%, protein 26%, carbohydrate 15,51% and other elements such as fiber and ash.The phytochemical analysis showed that the hexane-soluble seeds extract contained fatty acids, terpenoids and alkaloids, while the ethanol- soluble extract of the seeds contained alkaloids, steroids, terpenoids and saponins
Keyword: croton tiglium, tetradecaoic acid, myrictic acid, safety test, process technology

Judul: Extraction process engineering Kamandrah (Croton tiglium L.) seed oil with expression and biolarvacide development of Dengue Fever Preventive
Abstrak: Kamandrah (Croton triglium L.) is one of many medicinal plants found in the some parts of Indonesia. Kamandrah seeds produce oil that can be used as biolarvacide. The objectives of this research were to study larvacidal activity of the ingredients contained in the seed extract of kamandrah; to get optimum conditions of kamandrah seed extraction using pressing method; to provide processing technology of larvacide production; and to analyze the financial feasibility of the product. The results of proximate analysis of kamandrah seed oil showed that it contained 6.29% water, 3.6% ash, 53.73% fat, 11.98% protein, 8.25% crude fiber, and 16.15% carbohydrates (by difference). Kamandrah fruits harvested at the age of 42 days after flowering (fully brown rind colour) were the most effective as larvacide against A. aegepty larvae. The yield of oil was 20.42% and the LC50 value was as much as 132.67 ppm (24 hours) and 70.08 ppm (48 hours). The acid number of the oil was 8.76 mg KOH/g oil; free fatty acid level was 4.36 mg KOH/g oil; peroxide number was 3.59 meq O/100g; refractive index was 1.4783; specific gravity was 0.9466 g/ml and colour values were 73.03, 64.13, and 3.26 for L*, a* and b*, respectively. The two major unsaturated fatty acids components in kamandrah oil were oleic acid (42.33%) and linoleic acid (2.03%). The results of GC-MS analysis with NIST library search showed that the active ingredients predicted as insecticide were piperidine and 1,4- naphthoquinone while the result of identification with a library pest.l showed the the active ingredients were butacarboxim compound, 2,3,6-trichlorphenol, dnoc, and propamocarb. Based on optimization of kamandrah seed extraction process, the optimum conditions to obtain the optimum yield, LC50 and LC90 values using canonical analysis were heating temperature of 85oC, pressure of 10.54 Pa, and heating time of 15 minutes. The response values for yield, LC50 and LC90 at this optimum condition were 29%, 41,85 ppm, and 87,51 ppm, respectively. The results of product design for the plant-derived (vegetable) larvacide made from kamandrah oil showed that the best form was sustained released granules, which was non-irritating to eyes and skin, with LC50 values of 1,039 ppm (24 hours) and 718 ppm (48 hours). Financial analysis showed that the larvacide produced from oil extract of kamandrah seed was feasible to be developed and produced with NPV of Rp. 25.509.663.712, IRR of 32.9%, Net B/C ratio of 1.4 and PBP of 5.9 years.
Keyword: active ingredients, larvacide, oil extraction, financial analysis, Process Engineering, Kamandrah, Croton tiglium L., Seed Oil, Biolarvacide Development Of Dengue

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