Judul: Cellulose Triacetate Membrane Based Optical Sensor (Optode) As Iron Detector in Water
Abstrak: Besi merupakan logam berat yang sering dijumpai sebagai pencemar peraian. Konsentrasi besi yang tinggi bersifat toksik bagi kesehatan manusia, sehingga perlu dipantau keberadaannya dalam air. Pendeteksian besi dalam air dilakukan dengan memanfaatkan sensor optik (optode). Penelitian bertujuan memfabrikasi optode berbasis membran selulosa triasetat dengan reagen selektif terhadap Fe (III). Optode difabrikasi dengan mencampurkan polimer selulosa triasetat, pemlastis (asam oleat dan asetofenon), alikuot-336, dan tiosianat sebagai reagen selektifnya. Kinerja membran diuji berdasarkan rentang kerja, linearitas, batas deteksi dan kuantitasi, presisi, serta akurasi. Kinerja membran menunjukkan respon yang linear pada rentang konsentrasi 0,1–4 mg/L dengan koefisien determinasi (R2) 0,9937, batas deteksi 0,0250 mg/L, batas kuantitasi 0,0757 mg/L, presisi keterulangan dengan simpangan baku relatif 3,31%, dan rerata akurasi 100,49%. Selektivitas optode baik terhadap ion pengganggu Cr(VI) dan Pb(II). Warna kompleks optode stabil sampai hari ke-10. Aplikasi pendeteksian besi dalam sampel air menunjukkan rerata konsentrasi sebesar 0,2541 mg/L dengan presisi dan akurasi yang baik.
Keyword: cellulose triacetate, iron, optode, thiocyanate, water

Judul: Evaluasi Kinerja Metode Deteksi Besi dalam Air Menggunakan Membran Optode Berbasis Selulosa Triasetat
Abstrak: Besi merupakan logam berat yang mencemari lingkungan perairan dan dapat membahayakan kesehatan manusia. Oleh karena itu, diperlukan metode untuk mendeteksi kadar logam besi (Fe) di perairan dengan cepat dan mudah. Besi dideteksi menggunakan sensor optik (optode). Penelitian ini bertujuan memfabrikasi membran optode berbasis selulosa triasetat dengan menambahkan reagen selektif terhadap Fe(III). Optode dibuat dengan mencampurkan polimer selulosa triasetat (CTA) dengan tambahan pemlastis (asam oleat dan asetofenon), alikuot 336, dan ion tiosianat. Kinerja optode dievaluasi berdasarkan linearitas, batas deteksi dan kuantitasi, presisi, akurasi, dan selektivitas. Linearitas terbaik optode didapatkan koefisien determinasi (R2) 0,9972 pada konsentrasi 0,1–4,0 ppm. Batas deteksi dan kuantitasi diperoleh berturut-turut 9,88×10-7 M dan 0,1676 ppm, dengan presisi 3,01% dan akurasi 101,62±0,04%. Optode memiliki selektivitas yang baik terhadap ion penganggu Pb(II) dan Cr(VI). Aplikasi dalam sampel air menggunakan membran optode menunjukkan rerata konsentrasi 0,0578 ppm dan hasil pengujian tidak berbeda nyata dengan hasil deteksi menggunakan metode standar spektrofotometer UV-Vis, yaitu 0,0584 ppm., Iron is a heavy metal that pollutes the aquatic environment and threatens human health. Therefore, a quick and easy method is needed to detect iron (Fe) metal content in the aquatic environment. Iron detection can use an optical sensor (optode). This research aims to fabricate optode membranes based on cellulose triacetate with selective reagents for Fe(III). The optode was fabricated by mixing cellulose triacetate (CTA) polymer with plasticizer (oleic acid and acetophenone), aliquot 336, and thiocyanate ion. The performance was evaluated based on linearity, limit of detection and quantitation, precision, accuracy, and selectivity. The best optode linearity was obtained by a coefficient of determination (R2) of 0,9972 at a concentration of 0.1–4.0 ppm. The LOD and LOQ were 9,88×10-7 M and 0,1676 ppm, respectively, with a precision of 3.01% and an accuracy of 101.62±0,04%. The optode gave good selectivity for Pb(II) and Cr(VI) ions interfering. Application in water samples using the optode membrane showed an average concentration of 0.0578 ppm, and the results indicated no significant difference with the detection result using a standard UV- Vis spectrophotometer method, which was 0.0584 ppm.
Keyword: besi, evaluasi kinerja, optode, selulosa triasetat, tiosianat

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