Judul: Deteksi Senyawa Fenol dalam Air Permukaan dengan Teknik Biosensor Kertas
Abstrak: Keracunan fenol dapat mengakibatkan efek kesehatan pada paru-paru, ginjal, berbagai macam organ dan jaringan dan sistem kemih apabila melebihi nilai ambang batas yaitu sebesar 0,001 mg/L untuk perairan air tawar menurut Peraturan Pemerintah Republik Indonesia Nomor 82 Tahun 2001. Fenol biasanya diukur dengan menggunakan metode konvensional yang memiliki banyak kekurangan seperti biaya analisis yang mahal, waktu pengukuran yang lama, memerlukan analis yang kompeten, dan perawatan instrumen yang rumit sehingga diperlukan suatu alat yang cepat, murah, mudah dan sensitif dalam mengukur tingkat konsentrasi pencemaran fenol di lingkungan perairan secara biosensor. Keuntungan menggunakan tirosinase yaitu memiliki spesifisitas tinggi, interaksi enzim-substrat dan laju pergantian biokatalis yang tinggi seperti produk aktivitas katalis yang baik. Penelitian dilakukan secara non-eksperimental (studi literatur) dari beberapa jurnal terindeks scopus dan eksperimental (di laboratorium) dengan membuat biosensor kertas berbasis tirosinase dalam menguji fenolik pada sampel air bersih dan air sungai. Pembuatan biosensor ini melalui tiga tahapan, yaitu pembuatan biosensor kertas, uji kinerja biosensor, serta melakukan pengukuran sampel nyata. Biosensor didasarkan pada metode elektrostatis multilapis (Layer by Layer), yang menyebabkan pengendapan lapisan kitosan dan polielektrolit alginat sehingga menghasilkan reaksi enzimatis yang terimobilisasi pada permukaan kertas, respon dihitung sebagai perubahan warna merah muda menjadi biru keunguan pada awal penambahan fenol. Sensor menunjukan tingkat stabilitas 60 hari 97% pada suhu kamar (25 °C) lalu aktivitas turun menjadi 92% setelah 60 hari. Biosensor kertas tirosinase menghasilkan fungsionalitas yang baik saat pengukuran sampel nyata., Phenol poisoning can result in health effects of lungs, kidneys, various organs and tissues, and urinary system if it exceeds the threshold value of 0.001 mg/L for freshwater according to the Government Regulation of the Republic of Indonesia Number 82 of 2001. Measurement of phenol generally uses conventional methods but these methods have many shortcomings such as expensive analysis costs, long measurement times, require competent analysts, and complex instrument maintenance so that we need a tool that is fast, cheap, easy, and sensitive in measuring the level of phenol pollution concentrations in the aquatic environment. in biosensors. The advantage of using tyrosinase is that it has high specificity, enzyme-substrate interaction, and a high rate of biocatalyst turnover as a product of good catalyst activity. Thisresearch was conducted non-experimental (literature study) from several Scopus indexed journals and experimentally (in the laboratory) by making tyrosinase-based paper biosensors to test phenol in fresh water and river water samples. This biosensor was made through three stages, namely making paper biosensors, testing biosensor performance, and measuring real samples. Biosensors using the multi-layer electrostatic method (Layer by Layer) cause deposition of a layer of chitosan and polyelectrolyte alginate which results in an enzymatic reaction that is immobilized on the surface of the paper, the response is calculated as a change in color from pink to purplish-blue at the beginning of the addition of phenol. The sensor shows a 60-day stability rate of 97% at room temperature (25 ° C) then activity drops to 92% after 60 days. The paper tyrosinase biosensor provides good functionality when measuring realsamples.
Keyword: biosensor kertas, enzim, fenol, lingkungan

Judul: Biosensor Elektrokimia Berbasis Biofilm Bacillus licheniformis untuk Mendeteksi Senyawa Fenol
Abstrak: Fenol merupakan salah satu limbah berbahaya yang banyak dihasilkan dari berbagai aktivitas industri seperti industri tekstil, minyak bumi, farmasi, pestisida, dan plastik. Standar ambang batas fenol pada air minum adalah 0.002 ppm dan pada lingkungan perairan adalah 0.5-1 ppm. Metode alternatif yang dapat digunakan untuk mendeteksi fenol adalah menggunakan metode biosensor. Biosensor merupakan perangkat analisis yang mengintegrasikan biokomponen dengan transduser yang dapat mengubah sinyal hayati menjadi sinyal yang terukur. Penelitian ini bertujuan untuk membuat biosensor elektrokimia berbasis biofilm dari Bacillus licheniformis dengan menggunakan screen printed carbon electrode (SPCE) dan melakukan validasi kinerja biosensor tersebut. Hasil penelitian menunjukkan bahwa nilai koefisien determinasi yang diperoleh pada penelitian ini adalah 0.7287, sensitivitas 0.0089 mA ppm-1, dan presisi sebesar 1.2607 %SBR. Biosensor fenol berbasis biofilm Bacillus licheniformis memiliki presisi yang baik ditandai dengan nilai simpangan baku relatif yang rendah.
Keyword: Bacillus licheniformis, biofilm, biosensor, fenol, SPCE

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