Judul: Persepsi Konsumen mengenai Label Pangan serta Penggunaan Bahan Tambahan Pangan pada Produk Kopi Kemasan Siap Minum di DKI Jakarta
Abstrak: Permintaan terhadap produk olahan kopi semakin meningkat dan beragam, salah satunya kopi kemasan siap minum. Penelitian mengenai pengetahuan dan pemahaman konsumen tentang label pangan olahan pada produk kopi kemasan siap minum di DKI Jakarta masih terbatas, oleh karena itu perlu dilakukan survei mengenai pengetahuan dan pemahaman konsumen terhadap label pangan olahan serta identifikasi penggunaan BTP pada produk kopi kemasan siap minum. Tujuan penelitian ini yaitu mengkarakterisasi profil responden, mengeksplorasi dan mengkaji pengetahuan dan pemahaman konsumen (responden) terhadap label pangan olahan, menginvestigasi tingkat kepedulian konsumen (responden) terhadap pentingnya label pangan olahan, dan mengidentifikasi penggunaan BTP pada produk kopi kemasan siap minum di DKI Jakarta. Tahapan penelitian terdiri dari survei pengetahuan dan pemahaman konsumen mengenai label pangan olahan dan identifikasi penggunaan BTP. Hasil penelitian menunjukkan bahwa tingkat pengetahuan konsumen terhadap label pangan olahan termasuk ke dalam kategori cukup dan cenderung rendah. Tingkat kepedulian konsumen terhadap label pangan termasuk dalam kategori tinggi. BTP yang ditemukan saat pengamatan yaitu dari golongan antioksidan, pemanis, pengatur keasaman, pengawet, pengemulsi, penstabil, perisa, dan pewarna. BTP carry over yang ditemui yaitu dari jenis pewarna, antioksidan, dan pengawet. Terdapat ketidaksesuaian penggunaan BTP dengan kategori pangan dan golongan yang semestinya serta tidak sesuai dengan aturan penulisan pada label pangan olahan., The demand for processed coffee product is increasing and diverse, including ready-to-drink coffee. Research on consumer knowledge and understanding of processed food labels on ready-to-drink coffee products, especially in DKI Jakarta is still limited, therefore it is necessary to conduct a survey on consumer knowledge and understanding of processed food labels and identify the use of food additives in ready-to-drink coffee products. The purpose of this study was to characterized the respondent’s profile, explored and examined consumer (respondent) knowledge and understanding of processed food labels, investigated consumer (respondent) awareness of processed food labels and identified the use of food additives in readyto-drink coffee products in DKI Jakarta. This research stage consisted of survey of consumer knowledge and understanding about the label of processed food and identifying the usage of food additives in ready-to-drink coffee products. The results showed that the level of consumer knowledge of processed food labels was categorized sufficient and tended to be low. The level of consumer awareness of food labels was classified high. Food additives found during the observation were antioxidants, sweeteners, acidity regulators, preservatives, emulsifiers, stabilizers, flavours, and colourings. Carry over of food additives found during observation were colourings, antioxidants, and preservatives. There were types of food additives used that did not meet with the appropriate food categories and groups, as well as with the writing rules of processed food labels.
Keyword: Coffee, food additives, food labels, ready-to-drink, survey

Judul: Pemetaan Jenis Bahan Tambahan Pangan Produk Minuman Teh dan Pemenuhan Syarat Pelabelan serta Survei Konsumen di Bogor.
Abstrak: Minuman teh dalam kemasan merupakan minuman yang memiliki tingkat konsumsi tinggi di masyarakat. Pada proses pembuatannya, minuman teh menggunakan berbagai jenis bahan tambahan pangan. Tujuan penelitian ini adalah melakukan riset pasar terhadap bahan tambahan pangan yang digunakan pada produk minuman teh serta tingkat pemenuhan standar label pada kemasan dan survei konsumen di wilayah Bogor. Penelitian ini merupakan penelitian deskriptif dengan metode survei dan teknik pengambilan sampel purposive sampling. Pengolahan data menggunakan tabulasi data, SPSS 20, serta pembuatan diagram. Hasil penelitian terhadap 60 jenis produk dari 9 lokasi pengambilan sampel adalah pemanis (sukrosa), pengatur keasaman (natrium bikarbonat), perisa (perisa identik alami), antioksidan (asam askorbat), pengawet (kalium sorbat), penstabil nabati, sekuestran (natrium EDTA). Tingkat pemenuhan minimum label didapatkan sebesar 100%. Survei konsumen dilakukan menggunakan uji rangking, dan uji chi-square. Hasil survei menunjukkan responden pada kedua kelompok usia peduli terhadap bahan pengawet, masa kedaluwarsa pada label, serta keamanan pangan yang menjadi perhatian utama dalam pemilihan produk. Survei menunjukkan minuman teh merupakan produk dengan intensitas pembelian paling tinggi dibandingkan minuman ringan lainnya yang sejenis.
Keyword: BTP, label, minuman teh, riset pasar, survei konsumen

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