Judul: Pengembangan Produk Minuman Teh Hitam dan Teh Hijau dengan Penambahan Ekstrak Kulit Delima
Abstrak: Teh merupakan minuman yang telah lama diyakini memiliki khasiat bagi kesehatan tubuh. Delima memiliki kandungan senyawa polifenol yang paling banyak pada bagian kulitnya dibandingkan buah dan bijinya. Penelitian bertujuan mengembangkan formula teh hijau dan teh hitam dengan penambahan ekstrak kulit delima sebagai minuman fungsional, mengetahui tingkat kesukaan panelis dengan uji hedonik, mengetahui karakteristik warna minuman teh hijau dan teh hitam dengan penambahan ekstrak kulit delima, serta menganalisis kadar total fenolik formula minuman teh hijau dan teh hitam yang paling disukai. Taraf perlakuan konsentrasi ekstrak kulit delima yang ditambahkan pada teh hitam dan teh hijau yaitu sebanyak 0%, 0.5%, 1.5%, dan 2.5%. Uji warna menggunakan chromameter Minolta CR-400. Kadar total fenolik diuji dengan reagen Folin Ciocalteu. Setiap atribut uji rating hedonik pada minuman teh hitam dan teh hijau tidak memiliki perbedaan yang nyata pada setiap formula yang diuji sehingga dipilih formula yang mengandung ekstrak kulit delima sebanyak 2.5% untuk analisis total fenolik. Uji warna secara obyektif menghasilkan nilai L*, C*, h, serta ΔE*. Minuman teh hitam dan teh hijau dengan penambahan ekstrak kulit delima sebanyak 2.5% dapat meningkatkan kadar total fenolik minuman teh hitam dan teh hijau berturut-turut sebanyak 58.80% dan 57.11% menjadi sebesar 760.94±2.39 mg GAE/L dan 908.19±3.45 mg GAE/L.
Keyword: kulit delima, teh hijau, teh hitam, total fenolik

Judul: Optimization of Ready-to-Drink Black Tea Formula Using D-Optimal Mixture Design
Abstrak: Komoditas teh yang menjadi salah satu komoditas perkebunan unggulan di Indonesia semakin meluas minat konsumen di pasar dan semakin banyak jenis produk teh yang beredar. Oleh karena itu, untuk menemukan formulasi yang optimum teh hitam kemasan ready to drink menggunakan D-Optimal Mixture Design pada RSM. Respon sensori hedonik, sifat fisik, dan kadar polifenol pada produk perlu dipertimbangkan, kemudian produk dibandingkan dengan produk komersial. Berdasarkan batas bawah dan batas atas masing-masing bahan, teh hitam BOP (0,5-1,2%), teh hitam PF (0,2-0,9%), dan sukrosa (5,9-7,3%) diperoleh 17 rancangan formula dari piranti lunak Design Expert 12.0®. Uji sensori rating hedonik dengan skala penilaian 1-7 menggunakan teknik Balanced Incomplete Block Design (BIBD) dilakukan pada 68 panelis. Hasil dari proses optimasi diperoleh formula optimum dengan komposisi 0,5% teh hitam BOP, 0,9% teh hitam PF, dan 6,6% sukrosa dengan nilai desirability sebesar 0,722. Hasil uji sensori hedonik formula optimum memiliki target pasar konsumen yang berbeda dengan produk komersial. Kadar polifenol produk formula optimum yang terkandung sebesar 1029 mg/kg sehingga telah memenuhi untuk regulasi SNI minuman teh kemasan yaitu minimal 400 mg/kg.
Keyword: formula, polifenol, RTD, sensori, teh

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