Judul: Profil Trend Produk Minuman di E-Registration Badan POM Berdasarkan Kategori Pangan Beserta Hasil Evaluasi Kesesuaian Pelabelan di Kota Bogor.
Abstrak: Perkembangan industri minuman ditandai dengan berbagai jenis dan merek minuman yang mulai bervariasi. Pengeluaran rata-rata per kapita perbulan untuk pangan termasuk makanan dan minuman olahan juga meningkat dari tahun ke tahun. Sejalan dengan kebijakan pasar global, pengawasan harus dilakukan mulai dari produk masuk di entry point sampai beredar di pasar. Terjadi peningkatan jumlah izin edar produk pangan, namun tidak didukung oleh rendahnya minat konsumen dalam memperhatikan kelengkapan label yaitu sebanyak 6,7%. Penelitian ini bermaksud menentukan profil trend (kecenderungan) produk minuman saat ini yang terdaftar pada e-registration produk pangan Badan POM dan melakukan penilaian kesesuaian pelabelan trend produk minuman tersebut yang telah beredar di pasaran (post-market) secara kualitatif. Metode yang digunakan dalam melakukan pengambilan sampel adalah metode purposive sampling yang dilanjutkan dengan analisis kesesuaian pelabelan secara kualitatif dengan menggunakan formulir kesesuaian pelabelan berdasarkan Peraturan Kepala Badan POM Nomor 12 tentang Pendaftaran Pangan Olahan. Hasil pemetaan menunjukkan bahwa minuman teh mendominasi produk minuman terdaftar pada tahun 2014 sampai 2016 berdasarkan kategori pangan. Analisis kesesuaian pelabelan produk minuman teh di Kota Bogor menunjukkan tingginya sampel TMK secara keseluruhan sebanyak 88 sampel dari total 100 sampel yang dianalisis dengan sebagian besar didominasi oleh ketidaksesuaian pelabelan di bagian keterangan pada label.
Keyword: e-registration, label, pemetaan, post-market, profil

Judul: Pemenuhan Regulasi Pelabelan Produk Industri Rumah Tangga Pangan (IRTP) di Bogor.
Abstrak: This research was aimed to evaluate the degree of fulfillment of food product labels requirements by Small Medium Enterprises (SMEs) in Bogor compared with existing regulations (Indonesian Act Number 18 of 2012). Samples were chosen from three major SMEs products in Bogor, which were flour and its processed products; products from the process of grains and tubers; softdrink and powdered beverages. Four groups of elements were tested: (1) technical labeling, (2) content format, (3) minimum information displayed, and (4) prohibited information. The results showed the level of fulfillment for those three types of products were: (1) technical labeling: 44, 45, and 73%, (2) format content: 75, 80, and 60%, (3) minimum information: 71, 69, and 67%; and (4) not mention prohibited information: 99, 100, 96%, respectively. The average level of fulfillment for those three types of products were 72, 74, and 74%, respectively. This low level of fulfillment showed that further development of SMEs is needed, especially about food labelling criteria according to the existing regulations.
Keyword: SMEs, regulations, level of fulfillment, Label

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