Judul: Isolasi dan Identifikasi Senyawa Yang Berpotensi Sebagai Fitoestrogen Dari Daun Tabat Barito
Abstrak: Tabat barito (Ficus deltoidea) sering dijadikan abat tradisional dan jamu bagi kaum wan ita dan diduga mempunyai hubungan dengan hormon-harmon reproduksi wan ita, khususnya harmon estrogen. Kekurangan harmon estrogen dapat menyebabkan timbulnya penyakit jantung koroner dan ostereoporosis. Hal ini dapat diatasi dengan mengkonsumsi senyawa yang memiliki efek estrogenik dari tumbuhan, yang lebih dikenal dengan fitoestrogen. Senyawa fitoestrogen (isoflavon, lignan, peptida siklik) dapat diisolasi dari daun tabat barito. Isoflavon diisolasi dengan etanol yang dibubuhi Hel, Iignan diisolasi dengan metanol, peptida siklik diisolasi dengan etano!. Ketiga ekstrak tersebut diekstraksi kembali dengan etil asetat. Ekstrak etil asetat dillji pada selaput korioalantois tunas ayam untuk melihat kemampllannya menghambat pembentllkan pembulllh durah. Ekstrak yang paling aktif, yaitu ekstrak isotlavon difraksinasi dengan kromatografi kolom. Fraksifraksi yang didapat dari kromatografi koloiD diuji pad a selaput korioalantois tunas ayam untuk melihat keaktifannya. Uji bioaktivitas ini dilakukan dengan caru membuat jendela pada cangkang telur ayam bertunas lImur tiga hari. Pada hari ke-6 sam pel sebanyak 10 µl ditanamkan ke selaput korioalantois. Inkubasi dilanjutkan sampai had ke-8. Selanjutnya cangkang dibuka untuk melihat efek penghambatan pembuluh darah dibandingkan dengan kontrol. Uji titokimia untuk menunjukan adanya isoflavon dalam fniksi-fraksi terse but dilakukan dengan mencampurkan fraksi dengan serbuk Mg. Hel pekar, amil alkohol, kemudian dikocok dengan kuat. Warna merah yang terbentuk pada lapisan amil alkohol menunjukan adanya isotlavon. Dua fraksi teraktif dari tujuh fraksi, yaitu fraksi C dan fraksi D. Fraksi C diidentifikasi dengan kromatografi cair kinerja tinggi. Fraksi D diidentitikasi dengan kromatograti gas-spektroskopi massa (KG-SM).
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Judul: Optimalisasi ekstraksi komponen bioaktif daun tabat barito (Ficus deltoidea)
Abstrak: The plants may contain the important compound useful for human. Tabat barito (Ficus deltoidea) has been used as a traditional medicine. Previous study observed that the plant contains several active subtances like alkaloid and flavonoid subtances. This study was examined various extraction's conditions such as choice of solvent, ratio of solvent, extraction time and the division of solvent and time of extraction in order to produce an optimal yield of bioactive compound mixture. Extraction technique used in this research was a modification of Harhorne's method. There were three solvent used. Among them, methanol gave a higher yield (3.86 %) compared to ethyl acetate (2.26 %) and hexane (1.60 %). The ratio of solvent used were 1 :5, 1:7 and 1:9 and kept for 24, 48 and 72 hours. The higher solvent ratio and extraction time, the higher yield of extracts produced. However, the yield obtained from extraction using ratio 1:7 in 48 hours and 72 hours was slightly differed. Therefore, considering the cost of energy and time required for the process, the extraction time of 48 hours and solvent ratio 1:7 produces an optimum yield.
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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