Judul: Kajian Pengaruh Iradiasi Sinar Gamma Mortalitas Lalat Buah Dan Mutu Buah Mangga Gedong (Mangifera indica. L) Selama Penyimpanan
Abstrak: Mango cv. gedong is one of the potencial horticultural product to be exported, but mango is a host of fruit flies (Bactrocera dorsalis) that causing constrain with the quarantine regulation. Therefore, it need treatment to kill the fruit flies that invested inside the mango. Irradiaton treatment is one of disinfestation technique to kill the fruit flies add and keep fruit quality during storage. This research objective was to study the effect of irradiation gamma rays on fruit flies mortality and mango quality during storage. The doses at 0.25 kGy, 0.5 kGy, 0.75 kGy were used for mortality test on fruit flies invested mango. To see the effect of respiration and quality, the fruit were stored at 8, 13, 28 ºC (irradiation and control). The result shows that mortality of fruit flies B. papayae with the dose of 0.75 kGy reach 100%. Irradiation on the mango can decrease the growth of fruit fly, while on the control of manggo the fruit fly has been detected on the days 9th. The irradiotion treatment until the dose of 0.75 kGy is ineffective to eradicate the fruit fly that being infestations inside the mango, however, at 21 days of storage the fruit fly is still detected. The irradiation treatment with the dose of 0.75 kGy decrease respiration and postpone the fruit ripening also maintain the fruit quality based on the weight losses, the vitamin C content, total acid, TPT, and water content.
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Judul: Disinfestasi telur dan larva lalat buah pada buah mangga gedong gincu (Mangifera indica) dengan teknik perlakuan uap panas
Abstrak: The presence of fruitfly belongs to genus Bactrocera is the main problem of Indonesia’s program to export fresh mango. Subsequently, vapor heat treatment as one among suitable quarantine treatment methods had been developed to disinfest fruitfly in mango. This research was conducted with certain objectives such as: to observe thermotolerance of fruitfly against high temperature, to evaluate effect of treatment to the gedong gincu mango quality, and to determine the most effective level of vapor heat treatment that can be used to disinfest B. carambolae at laboratory scale. According to hot water dipping test, egg was the most tolerant stage, either on B. carambolae or B. papayae. In addition, eggs and larvae of B. papayae were more tolerant than those of B. carambolae at 46oC. While, increased temperatur comparison test show that mortality rate of B. carambolae larvae reach 100% after 20 minutes at 44ºC to 48ºC. On the other hand, all larvae were killed after 5 minutes treatment at 48ºC. There were no significant differences between treated and untreated gedong gincu mango in term of firmness, weight loss, peel color, sugar content and taste until 5 days after treatment at 45oC to 49oC. In laboratory scale, mortality of B. carambolae reach 100% at 46.5oC vapor heat on various holding time. This result show that vapor heat treatment at 46.5oC effectively killed eggs and larvae of B. carambolae. However, reducing the temperature might be effective if exposure time is prolonged. In the future, it is recommended to conduct large scale test for vapor heat treatment at 46.5o Keywords : fruitfly, gedong gincu mango, vapor heat treatment C as confirmatory test.
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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