Judul: Analisis Pendapat Responden (30 Mahasiswa Departemen Teknologi Industri Pertanian - FATETA, IPB) Terhadap Atribut Produk Sabun Mandi Kesehatan Transparan Merek Madu Mutiara
Abstrak: In these recent days transparent soaps are more popular as gift, souvenir and hotel soap. These kinds of soaps are sold to organizational consumer not for end user consumer. Actually transparent soap still potential to enter the retail market as soap that were intended for end user consumer, for example Madu Mutiara Soap which is positioned as health bath soap. The purpose of this research is for identifying attributes of transparent health bath soap Madu Mutiara which is important and influence respondent. This research is also purposed to analyze respondent opinion by using Fishbein Multi Attributes Model. Attributes of transparent health bath soap are the characteristics by which products are identified and differentiated. Product attributes usually comprise features, functions, benefits, and uses. Fishbein Multi Attributes Model is used to measure respondent attitude toward attributes of toilet transparent health soap Madu Mutiara. The result of this research indicates that cleansing power, aroma, colour, label, price, package, brand, advertisement, health and prestige are important factor and have quite influence for respondent in choosing transparent health bath soap product. Health is attribute that has the highest attitude value, which means that respondent think that health is the most important factor in choosing the product. On the contrary, prestige is attribute that has the lowest attitude value, this mean respondent think that prestige is the least important factor in choosing the product. This research also indicates that the total attitude value toward all attributes is 11,39 which means that respondent attitude toward transparent health bath soap Madu Mutiara is quite good. It shows that respondent still interest and keen on transparent health bath soap Madu Mutiara.
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Judul: Sifat Organoleptik Sabun  Transparan dengan Penambahan Madu
Abstrak: Honey is produced by honeybees and containing sugar more than 70%. The addition of honey in a transparent soap is expected to increase the clarity of transparent soap. The research was conducted to compare the organoleptic characteristic between transparent soap with addition of 7,5% honey (M2bee) in according with Qisti (2008) and Jannah (2008), and commercial transparent honey soap (Madoe). Hedonic, paired preference and descriptive tests used to analyzed the soap organoleptic characteristics. The data were analyzed with non parametric methods Kruskal-Wallis. The results showed that preference panels on the shape, color, clarity, moisture and softness as well as the whole performance’s “M2bee” were not significantly different to “Madoe”. The panelist could accept the shape, color, clarity, moisture effect and the whole performance of “M2bee”, but could not accept the odor, the bubbles, the freshess, and softness of “M2bee”. The panelist preferred the odor and clarity of “Madoe” to “M2bee”. “M2bee’s” odor were dominated by stearic acid and coconut oil. No growth of microorganisms found in the transparent soap with or without addition of 7,5% honey after 45 days storage. Keywords: transparent soap, honey, organoleptic
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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