Judul: Identifikasi Senyawa Aktif Tanaman Kamandrah (Croton tiglium) dan Biji Jarak pagar (Jatropha curcas) sebagai Larvasida Nabati Vektor Demam Berdarah Dengue
Abstrak: Telah dilakukan uji potensi larvasida nyamuk Aedes aegypti terhadap ekstrak air dan etanol dari daun kamandrah (Croton tiglium), batang kamandrah, biji kamandrah dan biji jarak pagar (Jatropha curcas), serta minyak kamandrah dan minyak jarak pagar. Ekstrak yang paling berpotensi diidentifikasi kandungan kimianya. Minyak kamandrah lebih berpotensi sebagai larvasida Aedes aegypti instar ke tiga di bandingkan dengan minyak jarak pagar, karena nilai LC50 minyak kamandrah lebih kecil dibandingkan dengan minyak jarak pagar. Nilai LC50 minyak kamandrah sebesar 769 ppm untuk 24 jam pengujian dan 384 ppm untuk 48 jam pengujian, sedangkan nilai LC50 minyak jarak pagar sebesar 1507 ppm untuk 24 jam pengujian dan 866 ppm untuk 48 jam pengujian. Ekstrak air dan etanol dari biji jarak pagar, daun kamandrah, batang kamandrah dan biji kamandrah tidak berpotensi sebagai larvasida. Identifikasi kandungan kimia dilakukan pada minyak kamandrah dan minyak jarak pagar dengan menggunakan GC-MS. Hasil analisa GC-MS diduga minyak kamandrah dan jarak pagar mengandung senyawa piperine dengan kemiripan library sebesar 83%-88%. Senyawa piperine adalah golongan alkaloid jenis piperidin yang biasa digunakan sebagai larvasida untuk nyamuk. Minyak kamandrah dan minyak jarak pagar dapat dikembangkan menjadi larvasida alami untuk mengontrol populasi nyamuk.
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Judul: Efektivitas Minyak Biji Kamandrah (Croton tiglium) Dan Jarak Pagar (Jatropha curcas) Sebagai Larvasida, Anti- Oviposisi Dan Ovisida Nyamuk Aedes aegypti Dan Aedes albopictus
Abstrak: Aedes aegypti and A. albopictus mosquito is increasing problem of public health, being the vector responsible for dengue and chikungunya. Various effort have been done to control the mosquitoes both chemically and naturally. Croton tiglium and Jatropha curcas seed oil were known to posses insecticide activity against larval mosquito species. The study was aimed to examine effectiveness of C. tiglium seed oil and J. curcas seed oil as larvicidal, anti-oviposition, and ovicidal against A. aegypti and A. albopictus, and quantitative analysis of piperine achieved by spectrophotometer. C. tiglium seed oil possessed a significantly higher larvicidal activity against the 3th-4th instar larvae of A. aegypti and A. albopictus than J. curcas seed oil. The larval mortality was observed after 24 h of exposure. The LC50 value of C. tiglium seed oil and J. curcas seed oil were 769.52 ppm and 1366.07 ppm respectively, against A. aegypti and 707.78 ppm and 905.29 ppm respectively, against A. albopictus. There was a decrease in the number of eggs laid by A. aegypti and A. albopictus with C. tiglium and J. curcas oil (≤ 5% eggs) if compared to control (> 50% eggs). C. tiglium and J. curcas seed oil was effective on 0.4%-0.5% concentration so that could prevent the eggs from being hatched (> 90%). Quantitative analysis of C. tiglium and J. curcas by spectrophotometer revealed the presence of piperine were 0.0385% and 0.0054%. The content of C. tiglium was higher compared to J. curcas as proved by the value of LC50. The result of this study suggested that the C. tiglium and J. curcas oil posses insecticide properties that could be developed and used as natural insecticide for larval mosquito control.
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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