Judul: Deteksi Tiamina dengan Tenik Differential Pulse Anodic Stripping Voltammetry Menggunakan Elektrode Karbon Kaca.
Abstrak: Tiamina merupakan senyawa esensial bagi perkembangan otak dan saraf. Mengingat pentingnya tiamina bagi kesehatan maka diperlukan suatu metode deteksi yang mudah dan cepat. Penelitian ini bertujuan mengembangkan metode deteksi tiamina dengan teknik differential pulse anodic stripping voltammetry menggunakan elektrode karbon kaca dan menguji cobakannya pada sampel suplemen. Penentuan kondisi optimum deteksi tiamina dilakukan meliputi parameter larutan elektrolit, potensial deposisi, dan waktu deposisi. Sementara evaluasi kinerja analitik metode dievaluasi meliputi parameter linearitas, presisi, limit deteksi dan limit kuantitasi. Kondisi optimum deteksi tiamina diperoleh pada pH 12, potensial deposisi -1600 mV(vs Ag/AgCl), dan waktu deposisi 240 s. Kinerja analitik elektrode karbon gelas pada deteksi tiamina memiliki nilai R2 0.994, %SBR 4.5669%, serta limit deteksi dan limit kuantitasi masing-masing 1.2×10-5 M dan 1.59×10-5M. Metode ini dapat digunakan untuk mengukur tiamina pada sampel suplemen vitamin B1 dengan persen ketepatan 95.96% dan sampel suplemen vitamin B kompleks dengan persen ketepatan 93.30%.
Keyword: glassy carbon electrode, supplement, thiamine, voltammetry

Judul: Pengembangan Metode Deteksi Tiamina Hidroklorida dengan Teknik Differential Pulse Voltammetry (DPV).
Abstrak: Tiamina merupakan mikronutrien yang berperan penting pada sistem syaraf dan metabolisme tubuh. Deteksi tiamina dapat dilakukan dengan teknik voltammetri pada suasana basa. Pada penelitian ini, dikembangkan metode deteksi tiamina dalam larutan pH netralmenggunakan teknik differential pulse voltammetry (DPV) pada elektrode karbon kaca. Kondisi optimum pengukuran tiamina ditentukan dengan memvariasikan parameter potensial praperlakuan, waktu praperlakuan, potensial step, potensial pulsa, dan waktu pulsa. Hasil penelitian menunjukkan puncak tiamina terdeteksi pada potensial 0.03 V vs Ag/AgCl. Kondisi pengukuran tiamina pada pH netral menggunakan metode DPV yang dipilih ialah pada potensial praperlakuan -1700 mV, waktu praperlakuan 10 s, potensial step 25 mV, dan potensial pulsa 200 mV. Evaluasi kinerja analitik pengukuran memberikan linearitas dengan nilai R2 sebesar 0.9809 pada rentang konsentrasi 0.6 mM s.d. 1.6 mM, limit deteksi sebesar 5.14 × 10-4M dan limit kuantitasi sebesar 1.71 × 10-3M. Presisi pengukuran tiamina memiliki nilai %SBR sebesar 4.4573%.Metode deteksi tiamina yang dikembangkan ini potensial untuk digunakan pada pengukuran tiamina secara rutin.
Keyword: differential pulse voltammetry, larutan netral, tiamina

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