Judul: Audir pemasaran formula pestisida alami neemba oil pada PT. Intaran Indonesia tahun 2004
Abstrak: Penelitian ini bertujuan untuk memeriksa atau melakukan audit terhadap hai- hal yang perlu diketahui diatas, lewat dua model audit pemasaran. Model pertama Competitive audit berdasarkan kerangka kerja The Strategic Marketing Plus 2000, yang terdiri dari dua instrumen. Instrumen pertama Competitive Setting Profile (CSP) akan menjawab pertanyaan pertama, dan pertanyaan kedua diaudit dengan instrumen Company Alignment Profile (CAP). Untuk pertanyaan ketiga, akan diaudit dengan instrument Marketing Effectiveness Review (MER), dari model Strategic Control. Hasil audit ini diharapkan dapat memberikan gambaran dan rekomendasi bagi pihak perusahaan sehingga dapat merumuskan strategi-strategi pemasaran yang jitu dan efektif
Keyword: 

Judul: Penerapan audit pemasaran dari markplus 2000 pada industri sayur organik di Jabodetabek (Kasus pada PT. Amani Mastra, Caman, Jatibening, Bekasi)
Abstrak: Lingkungan bisnis yang berubah kian cepat dan turbulen dan keberadaan pesaing yang mengancam, memaksa perusahaan untuk selalu secara berkala meninjau-ulang lingkungan bisnis untuk mengetahui apa yang sedang dan akan terjadi, dengan harapan setelah melakukan pengkajian pada lingkungan bisnis, perusahaan dapat mendesain strategi berdasarkan perubahan-perubahan yang terjadi di lingkungan bisnis tersebut. Kegiatan untuk meninjau-ulang lingkungan bisnis, mengidentifikasi apa yang sedang terjadi, perubahan apa yang akan dihadapi perusahaan, serta mendesain strategi berdasarkan perubahan yang terjadi ini. disebut dengan audit pemasaran (Markplus, 2002). Untuk sukses, dapat disimpulkan setiap perusahaan haruslah audit terhadap kegiatan pemasaran mereka.
Keyword: 

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