Judul: Analisis ekuitas merek air minum dalam kemasan (AMDK) di kota Bogor
Abstrak: Salah satu cara untuk mengatasi masalah perolehan air bersih, aman, sehat dan terjamin kebersihannya terutama di kota-kota besar adalah melalui produk air minum dalam kemasan, (AMDK) yang dibuat produsen minuman untuk memenuhi kebutuhan masyarakat akan air minum sehat. Selain untuk tujuan kesehatan, bagi sebagian besar masyarakat di kota-kota besar, keberadaan AMDK ini sangat dirasakan menguntungkan mengingat harganya yang terjangkau dan kepraktisan dalam mengkonsumsinya. Bisnis AMDK semakin menguntungkan, karena kebutuhan akan air minum terus meningkat seiring dengan pertumbuhan penduduk. Perusahaan yang bergerak dalam bisnis AMDK pun semakin banyak dan terus melakukan ekspansi untuk memperluas jaringan pasar produk-produknya, karena kebutuhan masyarakat akan air minum sangat tinggi, terutama di kota-kota besar tidak bisa lagi lepas dari AMDK. Semakin banyaknya jumlah industri yang bergerak dalam usaha AMDK di pasar, meningkat pula ketajaman persaingan di antara merek-merek AMDK yang beroperasi di pasar dan hanya produk AMDK yang memiliki brand equity kuat yang akan tetap mampu bersaing, merebut, dan menguasai pasar. Ekuitas merek merupakan bagian dari manajemen pemasaran yang berhubungan dengan manusia sebagai pasar sasaran. Prinsip pemasaran mengatakan bahwa tujuan pemasaran adalah memahami kebutuhan dan keinginan pelanggan dan memenuhinya secara lebih efisien dan efektif dibanding pesaing. Penelitian ini dilakukan untuk mengetahui tingkat ekuitas merek yang dihasilkan dari produk AMDK dalam menciptakan loyalitas pelanggan melalui riset elemen-elemen ekuitas merek. Elemen-elemen ekuitas merek tersebut meliputi kesadaran merek, asosiasi merek, persepsi kualitas dan loyalitas merek. Penelitian dilakukan di kota Bogor, Jawa Barat. Fokus merek produk yang diteliti pada penelitian ini adalah merek-merek produk AMDK yang dominan di pasaran yaitu merek Aqua, 2Tang, Ades dan Vit. Penelitian ini dilaksanakan pada bulan November-Desember 2003. Data yang digunakan adalah data primer dan sekunder. Obyek penelitian yang termasuk di dalam penelitian ini mempunyai ciri-ciri sebagai berikut responden yang sedang atau pernah mengkonsumsi AMDK, responden yang berusia antara 15 sampai 24 tahun, dan berdomisili di Kota Bogor. Jumlah responden yang dilibatkan berjumlah 100 orang.
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Judul: Analisis Tingkat Kesukaan Konsumen Terhadap  Air Minum Dalam Kemasan (Amdk)
Abstrak: Water is a natural resource that is necessary for the organism at both the plants, animals and even humans. Water is also the natural resources that are important in supporting economic development and social sectors such as industry, power, agriculture, fishery, animal husbandry, transport, tourism and households. Community needs to water at this time has been increasing. This is because the number of people who continue to grow. Growing number of people not accompanied by increased supply of drinking water can cause scarcity (Nurmalina, 2003). It makes business AMDK highly prospective and encourage industry growth AMDK in big cities in Indonesia. Based on the problems faced by the goal to be achieved from this research, namely, analyzing the level of consumers prefer to drink water in the product packaging (AMDK), and identify how the level of loyalty in the consumer packaged drinking water (AMDK). So with the introduction of this research are expected to provide benefits to companies AMDK producers, to consumers and to institutions. This research was conducted in the Village Babakan Raya Darmaga or around campus Darmaga IPB Bogor, in June until November 2008. The variables that are important in this research is the behavior of the community, community characteristics, market stimuli, and other stimuli. The indicators of public attitudes on the decision to purchase a product is the introduction of needs, information search, alternative evaluation, purchase decision and post-purchase behavior. From this research found that the results show that in principle the level of consumers prefer products AMDK is the same, because in principle, a sense of thirst can not be delayed. Level of consumer loyalty to products AMDK not stabilized, this is more due to factors such as availability of product and factor markets in preference to the price offered by the manufacturer AMDK itself. Based on the analysis in this research that there is no significant difference in the level of consumer preferences towards product AMDK tested. This showed in the results tested by the respondent that the test is done with the same information with the test performed without information about the brand of the product. Product purchase decisions based on the availability of more products in the market, the level of preference for the product itself.
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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