Judul: Karakteristik Pengeringan Beku Sari Temulawak (Curcuma xanthorrhiza Roxb.)
Abstrak: In this study, the process of freezing is done by using media temperature stratified. It aimed to see the influence of the freezing system with media temperature stratified to the characteristic and porosity of freeze dried curcuma extract products. The quality of the freeze dried curcuma extract was analyzed from the value of the level of curcumin and generated pore size. The results analyzed visually showed that the pore size to scenario 2 (Tma = -15ºC, Tmf = -20ºC, Tmb = -20ºC) was larger then scenario 1 (Tma = -15ºC, Tmf = -15ºC, Tmb = -20ºC) and scenario 1 was larger than the pore size in scenario 3 (Tma = -20ºC, Tmf = -20ºC, Tmb = -20ºC). The difference of pore size was affected by the moisture content of the material and freezing rate, although the freezing rate had no significant dependability because measuring is not sure. In addition, the influence of the freezing process does not show definite effect of the freeze drying characteristics. Content of curcumin that obtained after the freeze drying process already meet the standards, that is of 2.66%.
Keyword: 

Judul: Pengaruh Metode Pengeringan terhadap Karakteristik Fisiko - Kimia dan Sidik Jari FTIR Ekstrak Temu Kunci (Boesenbergia  pandurata)
Abstrak: Temu Kunci (Boesenbergia pandurata) merupakan tanaman rempah yang mengandung vitamin A, vitamin C, dan senyawa lainnya. Beberapa penelitian telah menyatakan bahwa temu kunci memiliki aktivitas antioksidan khususnya berasal dari senyawa fenolik. Antioksidan terbukti dapat menstabilkan radikal bebas dan menekan stres oksidatif sehingga mencegah berbagai penyakit, seperti kanker. Temu kunci segar memiliki karakteristik cepat rusak sehingga berbagai metode pengolahan dilakukan untuk memperpanjang umur simpan. Dalam penelitian ini, temu kunci segar dikeringkan dengan 4 perlakuan, yaitu pengeringan oven, oven vakum, freeze dryer, dan fluidized bed dryer. Penelitian ini bertujuan memperoleh informasi terkait perbandingan metode pengeringan antara oven, oven vakum, freeze dryer, dan fluidized bed dryer terhadap karakteristik fisiko (profil warna) dan kimia (aktivitas antioksidan metode DPPH, total fenol), serta sidik jari FTIR temu kunci. Simplisia temu kunci yang dihasilkan melalui metode freeze dryer memiliki profil warna kecerahan dan aktivitas antioksidan yang paling baik. Hasil analisis one way ANOVA menunjukkan bahwa metode pengeringan oven, oven vakum, freeze dryer, dan fluidized bed dryer memberikan pengaruh nyata (α < 0.05) terhadap aktivitas antioksidan, total fenol, serta profil warna. Spektrum inframerah keempat metode pengeringan menunjukkan adanya perbedaan dalam intensitas serapan, namun gugus fungsi yang terdeteksi masih serupa karena berada dalam rentang sama. Intensitas transmitan sampel perlakuan freeze dryer paling rendah dibandingkan perlakuan lainnya, aktivitas antioksidan yang tertinggi berasal dari sampel temu kunci. Perlakuan pengeringan menggunakan freeze dryer merupakan metode terbaik berdasarkan karakteristik fisiko – kimia ekstrak temu kunci, diikuti oleh Fluidized bed dryer., Fingeroot (Boesenbergia pandurata) is a spice plant that contains vitamin A, vitamin C, and other metabolites. Several studies have shown that fingeroot has antioxidant activity, particularly from phenolic compounds. Antioxidants are known to stabilize free radicals and reduce oxidative stress, thus preventing various diseases, such as cancer. Fresh fingeroot has a short shelf life and tends to deteriorate quickly. Therefore, various processing methods are used to extend its shelf life. In this study, fresh fingeroot was dried using four treatments: oven drying, vacuum oven drying, freeze drying, and fluidized bed drying. The study aimed to compare these drying methods in terms of physical characteristics (color profile) and chemical properties (DPPH antioxidant activity, total phenols) as well as the FTIR fingerprint of fingeroot.The freeze-dried fingeroot had the best color profile and antioxidant activity. One-way ANOVA analysis showed significant differences (α < 0.05) in antioxidant activity, total phenols, and color profile among the oven drying, vacuum oven drying, freeze drying, and fluidized bed drying methods. The infrared spectra of the four drying methods exhibited differences in absorption intensities, although the detected functional groups were similar as they were within the same range. The freeze-dried samples showed the lowest transmittance intensity compared to the other treatments, the highest antioxidant activity in the fingeroot samples. The drying treatment using a freeze dryer is the most superior method based on the physicochemical characteristics of the fingeroot extract, followed by the fluidized bed dryer.
Keyword: Antioxidant, color, drying, FTIR, spice

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