Judul: Strategi pemasaran kosmetika tradisional : Kasus PT Martina Berto, Jakarta
Abstrak: Industri kosmetika tradisional, adalah industri yang menggunakan bahan baku dari hasil-hasil pertanian. Industri kosmetika itu sendiri mempunyai prospek yang cukup cerah untuk dikembangkan. Sehingga industri yang bergerak dalam bidang kosmetika pun semakin banyak. Salah satu cara untuk mengantisipasi persaingan yang cukup ketat dalam industri ini adalah melalui strategi pemasaran yang dilaksanakan perusahaan. Penelitian ini bertujuan untuk melihat strategi pemasaran yang dilakukan oleh perusahaan kosmetika tradisional sekaligus mengetahui keragaan produksi perusahaan. Salah satu perusahaan yang termasuk dalam industri yang bergerak dalam bidang kosmetika tradisional adalah PT Martina Berto, yang berlokasi di Kawasan Industri Pulogadung, Jakarta. Hingga saat ini perusahaan mampu berproduksi rata- rata per bulan 300 400 ton, dengan penjualan rata-rata perbulan mencapai 80 110 persen dari total produksi, bahkan PT Martina Berto sendiri juga telah mampu mengeksport kosmetika ke luar negeri...
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Judul: Formulasi Strategi Pernasaran Produk Kosmetika Tradisional Melalui Analisis Faktor Internal dan Eksternal Perusahaan
Abstrak: Indonesia merupakan salah satu negara yang memiliki potensi agroindustri yang cukup besar. Salah satu produk agroindustri yang dapat meningkatkan nilai tambah hasil pertanian adalah produk kosmetika. Menurut Badan Pusat Statistik permintaan akan produk-produk kosmetika mencapai lebih dari 70.000 unit selama tahun 1999 (BPS, 1999). Besarnya permintaan akan produk kosmetika merupakan daya tarik industri ini, sehingga menjadi semakin berkembang.
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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