Judul: Pengaruh Pengetahuan dan Motivasi Mahasiswa terhadap Minat Beli produk Madoe honey IPB
Abstrak: Pengetahuan dan motivasi merupakan faktor yang memengaruhi minat beli. Tujuan penelitian ini adalah untuk mengetahui pengaruh pengetahuan dan motivasi konsumen terhadap minat beli produk Madoe honey IPB. Penelitian ini menggunakan desain cross sectional study yang dilakukan di IPB Dramaga, Jawa Barat pada bulan April 2014. Jumlah contoh 200 orang yang dipilih secara acak sederhana dengan jumlah proporsional berdasarkan fakultas. Pengetahuan mahasiswa tentang produk Madoe honey IPB tergolong masih rendah. Sedangkan motivasi mahasiswa memiliki motivasi yang cukup. Sebanyak 61,5 % mahasiswa berminat untuk membeli produk Madoe honey IPB. Pengetahuan dan motivasi memiiki hubungan dengan minat beli. Dari hasil uji pengaruh menunjukkan bahwa pengetahuan berpengaruh negatif signifikan terhadap minat beli artinya semakin tinggi pengetahuan maka minat beli akan semakin rendah sedangkan motivasi berpengaruh positif signifikan terhadap minat beli artinya semakin tinggi motivasi mahasiswa maka semakin tinggi pula minat beli.
Keyword: minat beli, motivasi, Madoe honey IPB, pengetahuan

Judul: Pengaruh Kelompok Acuan dan Pengetahuan terhadap Minat Beli Produk Pangan IPB pada Mahasiswa Program Sarjana
Abstrak: Bogor Agricultural University (IPB) is one of the university that produce many inovation product both in agriculture technology and foods. The purpose of this study was to analyze the influence of reference groups and knowledge toward intention to buy IPB’s food products on undergraduate students. The location of the research was in IPB Dramaga Bogor, West Java, in May 2012. Sampling technique that conducted in this research was simple random sampling and involved 400 students from all faculties in IPB as samples/participant. The data were analyzed using Chi-Square test, Pearson correlation, simple linear regression, and multiple linear regression. The results showed that one of the reference groups that most widely chosen was close friend (33,6%). More than half of the students were in a good enough categorized of knowledge (59,0%) and intention to buy (78,2%) IPB’s food product. Based on Pearson correlation test, reference groups had no correlation with knowledge (p=0,364) and intention to buy (p=0,376). However, knowledge had a positive and significantly correlated with intention to buy IPB’s food product (p=0,043). The result of regression test showed that intention to buy IPB’s food product effected by income (p=0,002) and knowledge (p=0,043)
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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